%%% -*-BibTeX-*-
%%% ====================================================================
%%% BibTeX-file{
%%%     author          = "Nelson H. F. Beebe",
%%%     version         = "1.48",
%%%     date            = "06 November 2014",
%%%     time            = "16:27:30 MDT",
%%%     filename        = "tcbb.bib",
%%%     address         = "University of Utah
%%%                        Department of Mathematics, 110 LCB
%%%                        155 S 1400 E RM 233
%%%                        Salt Lake City, UT 84112-0090
%%%                        USA",
%%%     telephone       = "+1 801 581 5254",
%%%     FAX             = "+1 801 581 4148",
%%%     URL             = "http://www.math.utah.edu/~beebe",
%%%     checksum        = "19236 34216 175415 1699464",
%%%     email           = "beebe at math.utah.edu, beebe at acm.org,
%%%                        beebe at computer.org (Internet)",
%%%     codetable       = "ISO/ASCII",
%%%     keywords        = "BibTeX; bibliography; IEEE/ACM Transactions
%%%                        on Computational Biology and
%%%                        Bioinformatics; TCBB",
%%%     license         = "public domain",
%%%     supported       = "yes",
%%%     docstring       = "This is a COMPLETE BibTeX bibliography for
%%%                        IEEE/ACM Transactions on Computational
%%%                        Biology and Bioinformatics (CODEN ITCBCY,
%%%                        ISSN 1545-5963 (print), 1557-9964
%%%                        (electronic)), covering all journal issues
%%%                        from 2004 to date.
%%%
%%%                        At version 1.48, the COMPLETE journal
%%%                        coverage looked like this:
%%%
%%%                             2004 (  23)    2008 (  59)    2012 ( 179)
%%%                             2005 (  37)    2009 (  52)    2013 ( 144)
%%%                             2006 (  41)    2010 (  75)    2014 (  72)
%%%                             2007 (  69)    2011 ( 155)
%%%
%%%                             Article:        906
%%%
%%%                             Total entries:  906
%%%
%%%                        The journal Web pages can be found at:
%%%
%%%                            http://www.acm.org/pubs/tcbb/
%%%                            http://portal.acm.org/browse_dl.cfm?idx=J954
%%%
%%%                        Qualified subscribers can retrieve the full
%%%                        text of recent articles in PDF form.
%%%
%%%                        The initial draft was extracted from the ACM
%%%                        Web pages.
%%%
%%%                        ACM copyrights explicitly permit abstracting
%%%                        with credit, so article abstracts, keywords,
%%%                        and subject classifications have been
%%%                        included in this bibliography wherever
%%%                        available.  Article reviews have been
%%%                        omitted, until their copyright status has
%%%                        been clarified.
%%%
%%%                        bibsource keys in the bibliography entries
%%%                        below indicate the entry originally came
%%%                        from the computer science bibliography
%%%                        archive, even though it has likely since
%%%                        been corrected and updated.
%%%
%%%                        URL keys in the bibliography point to
%%%                        World Wide Web locations of additional
%%%                        information about the entry.
%%%
%%%                        BibTeX citation tags are uniformly chosen
%%%                        as name:year:abbrev, where name is the
%%%                        family name of the first author or editor,
%%%                        year is a 4-digit number, and abbrev is a
%%%                        3-letter condensation of important title
%%%                        words. Citation tags were automatically
%%%                        generated by software developed for the
%%%                        BibNet Project.
%%%
%%%                        In this bibliography, entries are sorted in
%%%                        publication order, using ``bibsort -byvolume.''
%%%
%%%                        The checksum field above contains a CRC-16
%%%                        checksum as the first value, followed by the
%%%                        equivalent of the standard UNIX wc (word
%%%                        count) utility output of lines, words, and
%%%                        characters.  This is produced by Robert
%%%                        Solovay's checksum utility."
%%%     }
%%% ====================================================================

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                  Pav-lo-vic Pro-ko-pen-ko Rez-ar-ta Ri-bei-ro
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%%% ====================================================================
%%% Acknowledgement abbreviations:

@String{ack-nhfb = "Nelson H. F. Beebe,
                    University of Utah,
                    Department of Mathematics, 110 LCB,
                    155 S 1400 E RM 233,
                    Salt Lake City, UT 84112-0090, USA,
                    Tel: +1 801 581 5254,
                    FAX: +1 801 581 4148,
                    e-mail: \path|beebe@math.utah.edu|,
                            \path|beebe@acm.org|,
                            \path|beebe@computer.org| (Internet),
                    URL: \path|http://www.math.utah.edu/~beebe/|"}

%%% ====================================================================
%%% Journal abbreviations:

@String{j-TCBB                  = "IEEE\slash ACM Transactions on Computational
                                  Biology and Bioinformatics"}

%%% ====================================================================
%%% Bibliography entries:

@Article{Williams:2004:WM,
  author =       "Michael R. Williams",
  title =        "Welcome Message",
  journal =      j-TCBB,
  volume =       "1",
  number =       "1",
  pages =        "1--1",
  month =        jan,
  year =         "2004",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Nov 22 06:42:56 MST 2004",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Gusfield:2004:IIA,
  author =       "Dan Gusfield",
  title =        "Introduction to the {IEEE\slash ACM Transactions on
                 Computational Biology and Bioinformatics}",
  journal =      j-TCBB,
  volume =       "1",
  number =       "1",
  pages =        "2--3",
  month =        jan,
  year =         "2004",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Nov 22 06:42:56 MST 2004",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Williams:2004:INA,
  author =       "Michael R. Williams",
  title =        "Introduction of New {Associate Editors}",
  journal =      j-TCBB,
  volume =       "1",
  number =       "1",
  pages =        "4--12",
  month =        jan,
  year =         "2004",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Nov 22 06:42:56 MST 2004",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Moret:2004:PNM,
  author =       "Bernard M. E. Moret and Luay Nakhleh and Tandy Warnow
                 and C. Randal Linder and Anna Tholse and Anneke
                 Padolina and Jerry Sun and Ruth Timme",
  title =        "Phylogenetic Networks: Modeling, Reconstructibility,
                 and Accuracy",
  journal =      j-TCBB,
  volume =       "1",
  number =       "1",
  pages =        "13--23",
  month =        jan,
  year =         "2004",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Nov 22 06:42:56 MST 2004",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Madeira:2004:BAB,
  author =       "Sara C. Madeira and Arlindo L. Oliveira",
  title =        "Biclustering Algorithms for Biological Data Analysis:
                 {A} Survey",
  journal =      j-TCBB,
  volume =       "1",
  number =       "1",
  pages =        "24--45",
  month =        jan,
  year =         "2004",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Nov 22 06:42:56 MST 2004",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Preparata:2004:SHR,
  author =       "Franco P. Preparata",
  title =        "Sequencing-by-Hybridization Revisited: The
                 Analog-Spectrum Proposal",
  journal =      j-TCBB,
  volume =       "1",
  number =       "1",
  pages =        "46--52",
  month =        jan,
  year =         "2004",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Nov 22 06:42:56 MST 2004",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Hochsmann:2004:PMR,
  author =       "Matthias H{\"o}chsmann and Bj{\"o}rn Voss and Robert
                 Giegerich",
  title =        "Pure Multiple {RNA} Secondary Structure Alignments:
                 {A} Progressive Profile Approach",
  journal =      j-TCBB,
  volume =       "1",
  number =       "1",
  pages =        "53--62",
  month =        jan,
  year =         "2004",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Nov 22 06:42:56 MST 2004",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Anonymous:2004:INA,
  author =       "Anonymous",
  title =        "Introduction of New {Associate Editor}",
  journal =      j-TCBB,
  volume =       "1",
  number =       "2",
  pages =        "65--65",
  month =        apr,
  year =         "2004",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Nov 22 06:42:56 MST 2004",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Witwer:2004:PCR,
  author =       "Christina Witwer and Ivo L. Hofacker and Peter F.
                 Stadler",
  title =        "Prediction of Consensus {RNA} Secondary Structures
                 Including Pseudoknots",
  journal =      j-TCBB,
  volume =       "1",
  number =       "2",
  pages =        "66--77",
  month =        apr,
  year =         "2004",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Nov 22 06:42:56 MST 2004",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Bafna:2004:NRE,
  author =       "Vineet Bafna and Vikas Bansal",
  title =        "The Number of Recombination Events in a Sample
                 History: Conflict Graph and Lower Bounds",
  journal =      j-TCBB,
  volume =       "1",
  number =       "2",
  pages =        "78--90",
  month =        apr,
  year =         "2004",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Nov 22 06:42:56 MST 2004",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Raphael:2004:UPM,
  author =       "Benjamin Raphael and Lung-Tien Liu and George
                 Varghese",
  title =        "A Uniform Projection Method for Motif Discovery in
                 {DNA} Sequences",
  journal =      j-TCBB,
  volume =       "1",
  number =       "2",
  pages =        "91--94",
  month =        apr,
  year =         "2004",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Nov 22 06:42:56 MST 2004",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Gusfield:2004:INA,
  author =       "Dan Gusfield",
  title =        "Introduction of New {Associate Editors}",
  journal =      j-TCBB,
  volume =       "1",
  number =       "3",
  pages =        "97--97",
  month =        jul,
  year =         "2004",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jan 24 14:15:55 MST 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Scheid:2004:SDS,
  author =       "Stefanie Scheid and Rainer Spang",
  title =        "A Stochastic Downhill Search Algorithm for Estimating
                 the Local False Discovery Rate",
  journal =      j-TCBB,
  volume =       "1",
  number =       "3",
  pages =        "98--108",
  month =        jul,
  year =         "2004",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jan 24 14:15:55 MST 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Dress:2004:CSG,
  author =       "Andreas W. M. Dress and Daniel H. Huson",
  title =        "Constructing Splits Graphs",
  journal =      j-TCBB,
  volume =       "1",
  number =       "3",
  pages =        "109--115",
  month =        jul,
  year =         "2004",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jan 24 14:15:55 MST 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Cameron:2004:IGA,
  author =       "Michael Cameron and Hugh E. Williams and Adam
                 Cannane",
  title =        "Improved Gapped Alignment in {BLAST}",
  journal =      j-TCBB,
  volume =       "1",
  number =       "3",
  pages =        "116--129",
  month =        jul,
  year =         "2004",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jan 24 14:15:55 MST 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Evans:2004:UDT,
  author =       "Steven N. Evans and Tandy Warnow",
  title =        "Unidentifiable Divergence Times in Rates-across-Sites
                 Models",
  journal =      j-TCBB,
  volume =       "1",
  number =       "3",
  pages =        "130--134",
  month =        jul,
  year =         "2004",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jan 24 14:15:55 MST 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Kim:2004:GEW,
  author =       "Junhyong Kim and Inge Jonassen",
  title =        "Guest Editorial: {WABI} Special Section Part 1",
  journal =      j-TCBB,
  volume =       "1",
  number =       "4",
  pages =        "137--138",
  month =        oct,
  year =         "2004",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jan 24 14:15:55 MST 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Csuros:2004:MSS,
  author =       "Miklos Csuros",
  title =        "Maximum-Scoring Segment Sets",
  journal =      j-TCBB,
  volume =       "1",
  number =       "4",
  pages =        "139--150",
  month =        oct,
  year =         "2004",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jan 24 14:15:55 MST 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Huson:2004:PSN,
  author =       "Daniel H. Huson and Tobias Dezulian and Tobias Klopper
                 and Mike A. Steel",
  title =        "Phylogenetic Super-Networks from Partial Trees",
  journal =      j-TCBB,
  volume =       "1",
  number =       "4",
  pages =        "151--158",
  month =        oct,
  year =         "2004",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jan 24 14:15:55 MST 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Bannai:2004:ADO,
  author =       "Hideo Bannai and Heikki Hyyro and Ayumi Shinohara and
                 Masayuki Takeda and Kenta Nakai and Satoru Miyano",
  title =        "An {$ O(N^2) $} Algorithm for Discovering Optimal
                 {Boolean} Pattern Pairs",
  journal =      j-TCBB,
  volume =       "1",
  number =       "4",
  pages =        "159--170",
  month =        oct,
  year =         "2004",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jan 24 14:15:55 MST 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Gramm:2004:PTA,
  author =       "Jens Gramm",
  title =        "A Polynomial-Time Algorithm for the Matching of
                 Crossing Contact-Map Patterns",
  journal =      j-TCBB,
  volume =       "1",
  number =       "4",
  pages =        "171--180",
  month =        oct,
  year =         "2004",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jan 24 14:15:55 MST 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Ye:2004:UUD,
  author =       "Jieping Ye and Tao Li and Tao Xiong and Ravi
                 Janardan",
  title =        "Using Uncorrelated Discriminant Analysis for Tissue
                 Classification with Gene Expression Data",
  journal =      j-TCBB,
  volume =       "1",
  number =       "4",
  pages =        "181--190",
  month =        oct,
  year =         "2004",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jan 24 14:15:55 MST 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Anonymous:2004:AI,
  author =       "Anonymous",
  title =        "Annual Index",
  journal =      j-TCBB,
  volume =       "1",
  number =       "4",
  pages =        "191--192",
  month =        oct,
  year =         "2004",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jan 24 14:15:55 MST 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Kim:2005:GEW,
  author =       "Junhyong Kim and Inge Jonassen",
  title =        "Guest Editorial: {WABI} Special Section Part {II}",
  journal =      j-TCBB,
  volume =       "2",
  number =       "1",
  pages =        "1--2",
  month =        jan,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Tue Apr 12 07:11:54 MDT 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Allali:2005:NDH,
  author =       "Julien Allali and Marie-France Sagot",
  title =        "A New Distance for High Level {RNA} Secondary
                 Structure Comparison",
  journal =      j-TCBB,
  volume =       "2",
  number =       "1",
  pages =        "3--14",
  month =        jan,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Tue Apr 12 07:11:54 MDT 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Bertrand:2005:TRL,
  author =       "Denis Bertrand and Olivier Gascuel",
  title =        "Topological Rearrangements and Local Search Method for
                 Tandem Duplication Trees",
  journal =      j-TCBB,
  volume =       "2",
  number =       "1",
  pages =        "15--28",
  month =        jan,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Tue Apr 12 07:11:54 MDT 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Brown:2005:OMS,
  author =       "Daniel G. Brown",
  title =        "Optimizing Multiple Seeds for Protein Homology
                 Search",
  journal =      j-TCBB,
  volume =       "2",
  number =       "1",
  pages =        "29--38",
  month =        jan,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Tue Apr 12 07:11:54 MDT 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Gusfield:2005:EST,
  author =       "Dan Gusfield",
  title =        "Editorial-State of the Transaction",
  journal =      j-TCBB,
  volume =       "2",
  number =       "1",
  pages =        "39--39",
  month =        jan,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Tue Apr 12 07:11:54 MDT 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Pisanti:2005:BMG,
  author =       "Nadia Pisanti and Maxime Crochemore and Roberto Grossi
                 and Marie-France Sagot",
  title =        "Bases of Motifs for Generating Repeated Patterns with
                 Wild Cards",
  journal =      j-TCBB,
  volume =       "2",
  number =       "1",
  pages =        "40--50",
  month =        jan,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Tue Apr 12 07:11:54 MDT 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Kucherov:2005:MLF,
  author =       "Gregory Kucherov and Laurent Noe and Mikhail
                 Roytberg",
  title =        "Multiseed Lossless Filtration",
  journal =      j-TCBB,
  volume =       "2",
  number =       "1",
  pages =        "51--61",
  month =        jan,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Tue Apr 12 07:11:54 MDT 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Liu:2005:TMB,
  author =       "Ying Liu and Shamkant B. Navathe and Jorge Civera and
                 Venu Dasigi and Ashwin Ram and Brian J. Ciliax and Ray
                 Dingledine",
  title =        "Text Mining Biomedical Literature for Discovering
                 Gene-to-Gene Relationships: {A} Comparative Study of
                 Algorithms",
  journal =      j-TCBB,
  volume =       "2",
  number =       "1",
  pages =        "62--76",
  month =        jan,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Tue Apr 12 07:11:54 MDT 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Staff:2005:RL,
  author =       "{IEEE and ACM Transactions on Computational Biology
                 and Bioinformatics staff}",
  title =        "2004 Reviewers List",
  journal =      j-TCBB,
  volume =       "2",
  number =       "1",
  pages =        "77--77",
  month =        jan,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Tue Apr 12 07:11:54 MDT 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Ling:2005:GEIa,
  author =       "Charles X. Ling and William Stafford Noble and Qiang
                 Yang",
  title =        "{Guest Editors}' Introduction to the {Special Issue:
                 Machine Learning for Bioinformatics---Part 1}",
  journal =      j-TCBB,
  volume =       "2",
  number =       "2",
  pages =        "81--82",
  month =        apr,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jun 22 17:33:35 MDT 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Au:2005:ACG,
  author =       "Wai-Ho Au and Keith C. C. Chan and Andrew K. C. Wong
                 and Yang Wang",
  title =        "Attribute Clustering for Grouping, Selection, and
                 Classification of Gene Expression Data",
  journal =      j-TCBB,
  volume =       "2",
  number =       "2",
  pages =        "83--101",
  month =        apr,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jun 22 17:33:35 MDT 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Biyani:2005:JCP,
  author =       "Pravesh Biyani and Xiaolin Wu and Abhijit Sinha",
  title =        "Joint Classification and Pairing of Human
                 Chromosomes",
  journal =      j-TCBB,
  volume =       "2",
  number =       "2",
  pages =        "102--109",
  month =        apr,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jun 22 17:33:35 MDT 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Furlanello:2005:SLM,
  author =       "Cesare Furlanello and Maria Serafini and Stefano
                 Merler and Giuseppe Jurman",
  title =        "Semisupervised Learning for Molecular Profiling",
  journal =      j-TCBB,
  volume =       "2",
  number =       "2",
  pages =        "110--118",
  month =        apr,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jun 22 17:33:35 MDT 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Mamitsuka:2005:ELK,
  author =       "Hiroshi Mamitsuka",
  title =        "Essential Latent Knowledge for Protein-Protein
                 Interactions: Analysis by an Unsupervised Learning
                 Approach",
  journal =      j-TCBB,
  volume =       "2",
  number =       "2",
  pages =        "119--130",
  month =        apr,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jun 22 17:33:35 MDT 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Rajapakse:2005:MED,
  author =       "Jagath C. Rajapakse and Loi Sy Ho",
  title =        "{Markov} Encoding for Detecting Signals in Genomic
                 Sequences",
  journal =      j-TCBB,
  volume =       "2",
  number =       "2",
  pages =        "131--142",
  month =        apr,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jun 22 17:33:35 MDT 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Rogers:2005:LPD,
  author =       "Simon Rogers and Mark Girolami and Colin Campbell and
                 Rainer Breitling",
  title =        "The Latent Process Decomposition of {cDNA} Microarray
                 Data Sets",
  journal =      j-TCBB,
  volume =       "2",
  number =       "2",
  pages =        "143--156",
  month =        apr,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jun 22 17:33:35 MDT 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Xu:2005:FRP,
  author =       "Jinbo Xu",
  title =        "Fold Recognition by Predicted Alignment Accuracy",
  journal =      j-TCBB,
  volume =       "2",
  number =       "2",
  pages =        "157--165",
  month =        apr,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jun 22 17:33:35 MDT 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Shen:2005:DRB,
  author =       "Li Shen and Eng Chong Tan",
  title =        "Dimension Reduction-Based Penalized Logistic
                 Regression for Cancer Classification Using Microarray
                 Data",
  journal =      j-TCBB,
  volume =       "2",
  number =       "2",
  pages =        "166--175",
  month =        apr,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jun 22 17:33:35 MDT 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Ling:2005:GEIb,
  author =       "C. X. Ling and W. S. Noble and Q. Yang",
  title =        "{Guest Editor}'s Introduction to the {Special Issue:
                 Machine Learning for Bioinformatics---Part 2}",
  journal =      j-TCBB,
  volume =       "2",
  number =       "3",
  pages =        "177--178",
  month =        jul,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Tue Sep 20 06:11:25 MDT 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Schliep:2005:AGE,
  author =       "Alexander Schliep and Ivan G. Costa and Christine
                 Steinhoff and Alexander Schonhuth",
  title =        "Analyzing Gene Expression Time-Courses",
  journal =      j-TCBB,
  volume =       "2",
  number =       "3",
  pages =        "179--193",
  month =        jul,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Tue Sep 20 06:11:25 MDT 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Kundaje:2005:CST,
  author =       "Anshul Kundaje and Manuel Middendorf and Feng Gao and
                 Chris Wiggins and Christina Leslie",
  title =        "Combining Sequence and Time Series Expression Data to
                 Learn Transcriptional Modules",
  journal =      j-TCBB,
  volume =       "2",
  number =       "3",
  pages =        "194--202",
  month =        jul,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Tue Sep 20 06:11:25 MDT 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Kaski:2005:ACE,
  author =       "Samuel Kaski and Janne Nikkila and Janne Sinkkonen and
                 Leo Lahti and Juha E. A. Knuuttila and Christophe
                 Roos",
  title =        "Associative Clustering for Exploring Dependencies
                 between Functional Genomics Data Sets",
  journal =      j-TCBB,
  volume =       "2",
  number =       "3",
  pages =        "203--216",
  month =        jul,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Tue Sep 20 06:11:25 MDT 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Zhang:2005:PMF,
  author =       "Jingfen Zhang and Wen Gao and Jinjin Cai and Simin He
                 and Rong Zeng and Runsheng Chen",
  title =        "Predicting Molecular Formulas of Fragment Ions with
                 Isotope Patterns in Tandem Mass Spectra",
  journal =      j-TCBB,
  volume =       "2",
  number =       "3",
  pages =        "217--230",
  month =        jul,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Tue Sep 20 06:11:25 MDT 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Keedwell:2005:DGN,
  author =       "Edward Keedwell and Ajit Narayanan",
  title =        "Discovering Gene Networks with a Neural-Genetic
                 Hybrid",
  journal =      j-TCBB,
  volume =       "2",
  number =       "3",
  pages =        "231--242",
  month =        jul,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Tue Sep 20 06:11:25 MDT 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Hawkins:2005:ARN,
  author =       "John Hawkins and Mikael Boden",
  title =        "The Applicability of Recurrent Neural Networks for
                 Biological Sequence Analysis",
  journal =      j-TCBB,
  volume =       "2",
  number =       "3",
  pages =        "243--253",
  month =        jul,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Tue Sep 20 06:11:25 MDT 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Gustafsson:2005:CAL,
  author =       "Mika Gustafsson and Michael Hornquist and Anna
                 Lombardi",
  title =        "Constructing and Analyzing a Large-Scale Gene-to-Gene
                 Regulatory Network-Lasso-Constrained Inference and
                 Biological Validation",
  journal =      j-TCBB,
  volume =       "2",
  number =       "3",
  pages =        "254--261",
  month =        jul,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Tue Sep 20 06:11:25 MDT 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Demir:2005:LTP,
  author =       "Cigdem Demir and S. Humayun Gultekin and Bulent
                 Yener",
  title =        "Learning the Topological Properties of Brain Tumors",
  journal =      j-TCBB,
  volume =       "2",
  number =       "3",
  pages =        "262--270",
  month =        jul,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Tue Sep 20 06:11:25 MDT 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Anonymous:2005:CPS,
  author =       "Anonymous",
  title =        "Call for Papers for {Special Issue on Computational
                 Intelligence Approaches in Computational Biology and
                 Bioinformatics}",
  journal =      j-TCBB,
  volume =       "2",
  number =       "3",
  pages =        "271--271",
  month =        jul,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Tue Sep 20 06:11:25 MDT 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Cickovski:2005:FTD,
  author =       "Trevor M. Cickovski and Chengbang Huang and Rajiv
                 Chaturvedi and Tilmann Glimm and H. George E. Hentschel
                 and Mark S. Alber and James A. Glazier and Stuart A.
                 Newman and Jesus A. Izaguirre",
  title =        "A Framework for Three-Dimensional Simulation of
                 Morphogenesis",
  journal =      j-TCBB,
  volume =       "2",
  number =       "4",
  pages =        "273--288",
  month =        oct,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Nov 18 05:22:15 MST 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Boscolo:2005:GFN,
  author =       "Riccardo Boscolo and Chiara Sabatti and James C. Liao
                 and Vwani P. Roychowdhury",
  title =        "A Generalized Framework for Network Component
                 Analysis",
  journal =      j-TCBB,
  volume =       "2",
  number =       "4",
  pages =        "289--301",
  month =        oct,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Nov 18 05:22:15 MST 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Chen:2005:AOG,
  author =       "Xin Chen and Jie Zheng and Zheng Fu and Peng Nan and
                 Yang Zhong and Stefano Lonardi and Tao Jiang",
  title =        "Assignment of Orthologous Genes via Genome
                 Rearrangement",
  journal =      j-TCBB,
  volume =       "2",
  number =       "4",
  pages =        "302--315",
  month =        oct,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Nov 18 05:22:15 MST 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Turner:2005:BMS,
  author =       "Heather L. Turner and Trevor C. Bailey and Wojtek J.
                 Krzanowski and Cheryl A. Hemingway",
  title =        "Biclustering Models for Structured Microarray Data",
  journal =      j-TCBB,
  volume =       "2",
  number =       "4",
  pages =        "316--329",
  month =        oct,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Nov 18 05:22:15 MST 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Sevilla:2005:CBG,
  author =       "Jose L. Sevilla and Victor Segura and Adam Podhorski
                 and Elizabeth Guruceaga and Jose M. Mato and Luis A.
                 Martinez-Cruz and Fernando J. Corrales and Angel
                 Rubio",
  title =        "Correlation between Gene Expression and {GO} Semantic
                 Similarity",
  journal =      j-TCBB,
  volume =       "2",
  number =       "4",
  pages =        "330--338",
  month =        oct,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Nov 18 05:22:15 MST 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Yoon:2005:DCB,
  author =       "Sungroh Yoon and Christine Nardini and Luca Benini and
                 Giovanni De Micheli",
  title =        "Discovering Coherent Biclusters from Gene Expression
                 Data Using Zero-Suppressed Binary Decision Diagrams",
  journal =      j-TCBB,
  volume =       "2",
  number =       "4",
  pages =        "339--354",
  month =        oct,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Nov 18 05:22:15 MST 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Tseng:2005:EMG,
  author =       "Vincent S. Tseng and Ching-Pin Kao",
  title =        "Efficiently Mining Gene Expression Data via a Novel
                 Parameterless Clustering Method",
  journal =      j-TCBB,
  volume =       "2",
  number =       "4",
  pages =        "355--365",
  month =        oct,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Nov 18 05:22:15 MST 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Zhang:2005:SGN,
  author =       "Shaojie Zhang and Brian Haas and Eleazar Eskin and
                 Vineet Bafna",
  title =        "Searching Genomes for Noncoding {RNA} Using {FastR}",
  journal =      j-TCBB,
  volume =       "2",
  number =       "4",
  pages =        "366--379",
  month =        oct,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Nov 18 05:22:15 MST 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Anonymous:2005:AI,
  author =       "Anonymous",
  title =        "2005 Annual Index",
  journal =      j-TCBB,
  volume =       "2",
  number =       "4",
  pages =        "380--384",
  month =        oct,
  year =         "2005",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Nov 18 05:22:15 MST 2005",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Gusfield:2006:SJ,
  author =       "Dan Gusfield",
  title =        "State of the Journal",
  journal =      j-TCBB,
  volume =       "3",
  number =       "1",
  pages =        "1--1",
  month =        jan,
  year =         "2006",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2006.12",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Feb 16 11:06:15 MST 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Berger:2006:JAG,
  author =       "John A. Berger and Sampsa Hautaniemi and Sanjit K.
                 Mitra and Jaakko Astola",
  title =        "Jointly Analyzing Gene Expression and Copy Number Data
                 in Breast Cancer Using Data Reduction Models",
  journal =      j-TCBB,
  volume =       "3",
  number =       "1",
  pages =        "2--16",
  month =        jan,
  year =         "2006",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Feb 16 11:06:15 MST 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Sebastian:2006:STA,
  author =       "Rafael Sebastian and Maria-Elena Diaz and Guillermo
                 Ayala and Kresimir Letinic and Jose Moncho-Bogani and
                 Derek Toomre",
  title =        "Spatio-Temporal Analysis of Constitutive Exocytosis in
                 Epithelial Cells",
  journal =      j-TCBB,
  volume =       "3",
  number =       "1",
  pages =        "17--32",
  month =        jan,
  year =         "2006",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Feb 16 11:06:15 MST 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Hershkovitz:2006:SAR,
  author =       "Eli Hershkovitz and Guillermo Sapiro and Allen
                 Tannenbaum and Loren Dean Williams",
  title =        "Statistical Analysis of {RNA} Backbone",
  journal =      j-TCBB,
  volume =       "3",
  number =       "1",
  pages =        "33--46",
  month =        jan,
  year =         "2006",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Feb 16 11:06:15 MST 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Dawy:2006:GMM,
  author =       "Zaher Dawy and Bernhard Goebel and Joachim Hagenauer
                 and Christophe Andreoli and Thomas Meitinger and Jakob
                 C. Mueller",
  title =        "Gene Mapping and Marker Clustering Using {Shannon}'s
                 Mutual Information",
  journal =      j-TCBB,
  volume =       "3",
  number =       "1",
  pages =        "47--56",
  month =        jan,
  year =         "2006",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2006.9",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Jun 7 15:19:59 MDT 2013",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/bibnet/authors/s/shannon-claude-elwood.bib;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Goutsias:2006:HMM,
  author =       "John Goutsias",
  title =        "A Hidden {Markov} Model for Transcriptional Regulation
                 in Single Cells",
  journal =      j-TCBB,
  volume =       "3",
  number =       "1",
  pages =        "57--71",
  month =        jan,
  year =         "2006",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Feb 16 11:06:15 MST 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Rueda:2006:HCA,
  author =       "Luis Rueda and Vidya Vidyadharan",
  title =        "A Hill-Climbing Approach for Automatic Gridding of
                 {cDNA} Microarray Images",
  journal =      j-TCBB,
  volume =       "3",
  number =       "1",
  pages =        "72--83",
  month =        jan,
  year =         "2006",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Feb 16 11:06:15 MST 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Semple:2006:UNC,
  author =       "Charles Semple and Mike Steel",
  title =        "Unicyclic Networks: Compatibility and Enumeration",
  journal =      j-TCBB,
  volume =       "3",
  number =       "1",
  pages =        "84--91",
  month =        jan,
  year =         "2006",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Feb 16 11:06:15 MST 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Roch:2006:SPP,
  author =       "Sebastien Roch",
  title =        "A Short Proof that Phylogenetic Tree Reconstruction by
                 Maximum Likelihood Is Hard",
  journal =      j-TCBB,
  volume =       "3",
  number =       "1",
  pages =        "92--94",
  month =        jan,
  year =         "2006",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Feb 16 11:06:15 MST 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Anonymous:2006:RL,
  author =       "Anonymous",
  title =        "2005 Reviewers List",
  journal =      j-TCBB,
  volume =       "3",
  number =       "1",
  pages =        "95--96",
  month =        jan,
  year =         "2006",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Feb 16 11:06:15 MST 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Gusfield:2006:INA,
  author =       "Dan Gusfield",
  title =        "Introduction of New {Associate Editors}",
  journal =      j-TCBB,
  volume =       "3",
  number =       "2",
  pages =        "97--97",
  month =        apr,
  year =         "2006",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2006.25",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jun 7 06:38:18 MDT 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Chu:2006:BSM,
  author =       "Wei Chu and Zoubin Ghahramani and Alexei
                 Podtelezhnikov and David L. Wild",
  title =        "{Bayesian} Segmental Models with Multiple Sequence
                 Alignment Profiles for Protein Secondary Structure and
                 Contact Map Prediction",
  journal =      j-TCBB,
  volume =       "3",
  number =       "2",
  pages =        "98--113",
  month =        apr,
  year =         "2006",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2006.17",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jun 7 06:38:18 MDT 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Danziger:2006:FCM,
  author =       "Samuel A. Danziger and S. Joshua Swamidass and Jue
                 Zeng and Lawrence R. Dearth and Qiang Lu and Jonathan
                 H. Chen and Jianlin Cheng and Vinh P. Hoang and Hiroto
                 Saigo and Ray Luo and Pierre Baldi and Rainer K.
                 Brachmann and Richard H. Lathrop",
  title =        "Functional Census of Mutation Sequence Spaces: The
                 Example of p53 Cancer Rescue Mutants",
  journal =      j-TCBB,
  volume =       "3",
  number =       "2",
  pages =        "114--125",
  month =        apr,
  year =         "2006",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2006.22",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jun 7 06:38:18 MDT 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Carvalho:2006:EAI,
  author =       "Alexandra M. Carvalho and Ana T. Freitas and Arlindo
                 L. Oliveira and Marie-France Sagot",
  title =        "An Efficient Algorithm for the Identification of
                 Structured Motifs in {DNA} Promoter Sequences",
  journal =      j-TCBB,
  volume =       "3",
  number =       "2",
  pages =        "126--140",
  month =        apr,
  year =         "2006",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2006.16",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jun 7 06:38:18 MDT 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Brown:2006:IPA,
  author =       "Daniel G. Brown and Ian M. Harrower",
  title =        "Integer Programming Approaches to Haplotype Inference
                 by Pure Parsimony",
  journal =      j-TCBB,
  volume =       "3",
  number =       "2",
  pages =        "141--154",
  month =        apr,
  year =         "2006",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2006.24",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jun 7 06:38:18 MDT 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Vass:2006:JMB,
  author =       "Marc T. Vass and Clifford A. Shaffer and Naren
                 Ramakrishnan and Layne T. Watson and John J. Tyson",
  title =        "The {JigCell} Model Builder: {A} Spreadsheet Interface
                 for Creating Biochemical Reaction Network Models",
  journal =      j-TCBB,
  volume =       "3",
  number =       "2",
  pages =        "155--164",
  month =        apr,
  year =         "2006",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2006.27",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jun 7 06:38:18 MDT 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Chen:2006:MFS,
  author =       "Duhong Chen and Oliver Eulenstein and David
                 Fernandez-Baca and Michael Sanderson",
  title =        "Minimum-Flip Supertrees: Complexity and Algorithms",
  journal =      j-TCBB,
  volume =       "3",
  number =       "2",
  pages =        "165--173",
  month =        apr,
  year =         "2006",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2006.26",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jun 7 06:38:18 MDT 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Sevon:2006:TTP,
  author =       "Petteri Sevon and Hannu Toivonen and Vesa Ollikainen",
  title =        "{TreeDT}: Tree Pattern Mining for Gene Mapping",
  journal =      j-TCBB,
  volume =       "3",
  number =       "2",
  pages =        "174--185",
  month =        apr,
  year =         "2006",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2006.28",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jun 7 06:38:18 MDT 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Song:2006:CNS,
  author =       "Yun S. Song",
  title =        "A Concise Necessary and Sufficient Condition for the
                 Existence of a Galled-Tree",
  journal =      j-TCBB,
  volume =       "3",
  number =       "2",
  pages =        "186--191",
  month =        apr,
  year =         "2006",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2006.15",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jun 7 06:38:18 MDT 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Galled-trees are a special class of graphical
                 representation of evolutionary history that has proven
                 amenable to efficient, polynomial-time algorithms. The
                 goal of this paper is to construct a concise necessary
                 and sufficient condition for the existence of a
                 galled-tree for $M$, a set of binary sequences that
                 purportedly have evolved in the presence of
                 recombination. Both root-known and root-unknown cases
                 are considered here.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Daras:2006:TDS,
  author =       "Petros Daras and Dimitrios Zarpalas and Apostolos
                 Axenopoulos and Dimitrios Tzovaras and Michael
                 Gerassimos Strintzis",
  title =        "Three-Dimensional Shape-Structure Comparison Method
                 for Protein Classification",
  journal =      j-TCBB,
  volume =       "3",
  number =       "3",
  pages =        "193--207",
  month =        jul,
  year =         "2006",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Sep 11 07:36:29 MDT 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Yu:2006:MPA,
  author =       "Weichuan Yu and Xiaoye Li and Junfeng Liu and Baolin
                 Wu and Kenneth R. Williams and Hongyu Zhao",
  title =        "Multiple Peak Alignment in Sequential Data Analysis:
                 {A} Scale-Space-Based Approach",
  journal =      j-TCBB,
  volume =       "3",
  number =       "3",
  pages =        "208--219",
  month =        jul,
  year =         "2006",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Sep 11 07:36:29 MDT 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Abul:2006:PAE,
  author =       "Osman Abul and Reda Alhajj and Faruk Polat",
  title =        "A Powerful Approach for Effective Finding of
                 Significantly Differentially Expressed Genes",
  journal =      j-TCBB,
  volume =       "3",
  number =       "3",
  pages =        "220--231",
  month =        jul,
  year =         "2006",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Sep 11 07:36:29 MDT 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Nagarajan:2006:CSC,
  author =       "Radhakrishnan Nagarajan and Meenakshi Upreti",
  title =        "Correlation Statistics for {cDNA} Microarray Image
                 Analysis",
  journal =      j-TCBB,
  volume =       "3",
  number =       "3",
  pages =        "232--238",
  month =        jul,
  year =         "2006",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Sep 11 07:36:29 MDT 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Song:2006:CAP,
  author =       "Yun S. Song and Rune Lyngso and Jotun Hein",
  title =        "Counting All Possible Ancestral Configurations of
                 Sample Sequences in Population Genetics",
  journal =      j-TCBB,
  volume =       "3",
  number =       "3",
  pages =        "239--251",
  month =        jul,
  year =         "2006",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Sep 11 07:36:29 MDT 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Pirinen:2006:FCG,
  author =       "Matti Pirinen and Dario Gasbarra",
  title =        "Finding Consistent Gene Transmission Patterns on Large
                 and Complex Pedigrees",
  journal =      j-TCBB,
  volume =       "3",
  number =       "3",
  pages =        "252--262",
  month =        jul,
  year =         "2006",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Sep 11 07:36:29 MDT 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Popescu:2006:FMG,
  author =       "Mihail Popescu and James M. Keller and Joyce A.
                 Mitchell",
  title =        "Fuzzy Measures on the Gene Ontology for Gene Product
                 Similarity",
  journal =      j-TCBB,
  volume =       "3",
  number =       "3",
  pages =        "263--274",
  month =        jul,
  year =         "2006",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Sep 11 07:36:29 MDT 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Bernt:2006:GRB,
  author =       "Matthias Bernt and Daniel Merkle and Martin
                 Middendorf",
  title =        "Genome Rearrangement Based on Reversals that Preserve
                 Conserved Intervals",
  journal =      j-TCBB,
  volume =       "3",
  number =       "3",
  pages =        "275--288",
  month =        jul,
  year =         "2006",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Sep 11 07:36:29 MDT 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Berry:2006:IPC,
  author =       "Vincent Berry and Fran{\c{c}}ois Nicolas",
  title =        "Improved Parameterized Complexity of the Maximum
                 Agreement Subtree and Maximum Compatible Tree
                 Problems",
  journal =      j-TCBB,
  volume =       "3",
  number =       "3",
  pages =        "289--302",
  month =        jul,
  year =         "2006",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Sep 11 07:36:29 MDT 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Sharan:2006:ITP,
  author =       "Roded Sharan and Bjarni V. Halldorsson and Sorin
                 Istrail",
  title =        "Islands of Tractability for Parsimony Haplotyping",
  journal =      j-TCBB,
  volume =       "3",
  number =       "3",
  pages =        "303--311",
  month =        jul,
  year =         "2006",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Sep 11 07:36:29 MDT 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Zhang:2006:SGR,
  author =       "Chaolin Zhang and Xuesong Lu and Xuegong Zhang",
  title =        "Significance of Gene Ranking for Classification of
                 Microarray Samples",
  journal =      j-TCBB,
  volume =       "3",
  number =       "3",
  pages =        "312--320",
  month =        jul,
  year =         "2006",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Sep 11 07:36:29 MDT 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Casadio:2006:GEI,
  author =       "Rita Casadio",
  title =        "{Guest Editor}'s Introduction to the Special Issue on
                 Computational Biology and Bioinformatics -- Part 1",
  journal =      j-TCBB,
  volume =       "3",
  number =       "4",
  pages =        "321--322",
  month =        oct,
  year =         "2006",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Nov 30 19:05:58 MST 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Snir:2006:UMC,
  author =       "Sagi Snir and Satish Rao",
  title =        "Using Max Cut to Enhance Rooted Trees Consistency",
  journal =      j-TCBB,
  volume =       "3",
  number =       "4",
  pages =        "323--333",
  month =        oct,
  year =         "2006",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Nov 30 19:05:58 MST 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Ganapathy:2006:PIB,
  author =       "Ganeshkumar Ganapathy and Barbara Goodson and Robert
                 Jansen and Hai-son Le and Vijaya Ramachandran and Tandy
                 Warnow",
  title =        "Pattern Identification in Biogeography",
  journal =      j-TCBB,
  volume =       "3",
  number =       "4",
  pages =        "334--346",
  month =        oct,
  year =         "2006",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Nov 30 19:05:58 MST 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Wernicke:2006:EDN,
  author =       "Sebastian Wernicke",
  title =        "Efficient Detection of Network Motifs",
  journal =      j-TCBB,
  volume =       "3",
  number =       "4",
  pages =        "347--359",
  month =        oct,
  year =         "2006",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Nov 30 19:05:58 MST 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Lacroix:2006:MSG,
  author =       "Vincent Lacroix and Cristina G. Fernandes and
                 Marie-France Sagot",
  title =        "Motif Search in Graphs: Application to Metabolic
                 Networks",
  journal =      j-TCBB,
  volume =       "3",
  number =       "4",
  pages =        "360--368",
  month =        oct,
  year =         "2006",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Nov 30 19:05:58 MST 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Elias:2006:AAS,
  author =       "Isaac Elias and Tzvika Hartman",
  title =        "A $ 1.375 $-Approximation Algorithm for Sorting by
                 Transpositions",
  journal =      j-TCBB,
  volume =       "3",
  number =       "4",
  pages =        "369--379",
  month =        oct,
  year =         "2006",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Nov 30 19:05:58 MST 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Labarre:2006:NBT,
  author =       "Anthony Labarre",
  title =        "New Bounds and Tractable Instances for the
                 Transposition Distance",
  journal =      j-TCBB,
  volume =       "3",
  number =       "4",
  pages =        "380--394",
  month =        oct,
  year =         "2006",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Nov 30 19:05:58 MST 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Sammeth:2006:CTR,
  author =       "Michael Sammeth and Jens Stoye",
  title =        "Comparing Tandem Repeats with Duplications and
                 Excisions of Variable Degree",
  journal =      j-TCBB,
  volume =       "3",
  number =       "4",
  pages =        "395--407",
  month =        oct,
  year =         "2006",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Nov 30 19:05:58 MST 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Bilu:2006:FAO,
  author =       "Yonatan Bilu and Pankaj K. Agarwal and Rachel
                 Kolodny",
  title =        "Faster Algorithms for Optimal Multiple Sequence
                 Alignment Based on Pairwise Comparisons",
  journal =      j-TCBB,
  volume =       "3",
  number =       "4",
  pages =        "408--422",
  month =        oct,
  year =         "2006",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Nov 30 19:05:58 MST 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Song:2006:EPA,
  author =       "Yinglei Song and Chunmei Liu and Xiuzhen Huang and
                 Russell L. Malmberg and Ying Xu and Liming Cai",
  title =        "Efficient Parameterized Algorithms for Biopolymer
                 Structure-Sequence Alignment",
  journal =      j-TCBB,
  volume =       "3",
  number =       "4",
  pages =        "423--432",
  month =        oct,
  year =         "2006",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Nov 30 19:05:58 MST 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Anonymous:2006:AI,
  author =       "Anonymous",
  title =        "Annual Index",
  journal =      j-TCBB,
  volume =       "3",
  number =       "4",
  pages =        "??--??",
  month =        oct,
  year =         "2006",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Nov 30 19:05:58 MST 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Gusfield:2007:SJ,
  author =       "Dan Gusfield",
  title =        "State of the {Journal}",
  journal =      j-TCBB,
  volume =       "4",
  number =       "1",
  pages =        "1--1",
  month =        jan,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:57:20 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Gusfield:2007:AEAa,
  author =       "Dan Gusfield",
  title =        "{Associate Editor} Appreciation and Welcome",
  journal =      j-TCBB,
  volume =       "4",
  number =       "1",
  pages =        "2--2",
  month =        jan,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:57:20 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Casadio:2007:GEI,
  author =       "Rita Casadio",
  title =        "{Guest Editor}'s Introduction to the {Special Section
                 on Computational Biology and Bioinformatics (WABI)} --
                 Part 2",
  journal =      j-TCBB,
  volume =       "4",
  number =       "1",
  pages =        "3--3",
  month =        jan,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:57:20 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Berard:2007:PSR,
  author =       "Severine B{\'e}rard and Anne Bergeron and Cedric
                 Chauve and Christophe Paul",
  title =        "Perfect Sorting by Reversals Is Not Always Difficult",
  journal =      j-TCBB,
  volume =       "4",
  number =       "1",
  pages =        "4--16",
  month =        jan,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:57:20 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "We propose new algorithms for computing pairwise
                 rearrangement scenarios that conserve the combinatorial
                 structure of genomes. More precisely, we investigate
                 the problem of sorting signed permutations by reversals
                 without breaking common intervals. We describe a
                 combinatorial framework for this problem that allows us
                 to characterize classes of signed permutations for
                 which one can compute, in polynomial time, a shortest
                 reversal scenario that conserves all common intervals.
                 In particular, we define a class of permutations for
                 which this computation can be done in linear time with
                 a very simple algorithm that does not rely on the
                 classical Hannenhalli-Pevzner theory for sorting by
                 reversals. We apply these methods to the computation of
                 rearrangement scenarios between permutations obtained
                 from 16 synteny blocks of the X chromosomes of the
                 human, mouse, and rat.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "common intervals; evolution scenarios; reversals",
}

@Article{Vashist:2007:OCM,
  author =       "Akshay Vashist and Casimir A. Kulikowski and Ilya
                 Muchnik",
  title =        "Ortholog Clustering on a Multipartite Graph",
  journal =      j-TCBB,
  volume =       "4",
  number =       "1",
  pages =        "17--27",
  month =        jan,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:57:20 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "We present a method for automatically extracting
                 groups of orthologous genes from a large set of genomes
                 by a new clustering algorithm on a weighted
                 multipartite graph. The method assigns a score to an
                 arbitrary subset of genes from multiple genomes to
                 assess the orthologous relationships between genes in
                 the subset. This score is computed using sequence
                 similarities between the member genes and the
                 phylogenetic relationship between the corresponding
                 genomes. An ortholog cluster is found as the subset
                 with the highest score, so ortholog clustering is
                 formulated as a combinatorial optimization problem. The
                 algorithm for finding an ortholog cluster runs in time
                 $ O(|E| + |V| l o g|V|) $, where $V$ and $E$ are the
                 sets of vertices and edges, respectively, in the graph.
                 However, if we discretize the similarity scores into a
                 constant number of bins, the runtime improves to $
                 O(|E| + |V|) $. The proposed method was applied to
                 seven complete eukaryote genomes on which the manually
                 curated database of eukaryotic ortholog clusters, KOG,
                 is constructed. A comparison of our results with the
                 manually curated ortholog clusters shows that our
                 clusters are well correlated with the existing
                 clusters.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "biology; clustering algorithms; genetics;
                 Graph-theoretic methods",
}

@Article{Lasker:2007:EDH,
  author =       "Keren Lasker and Oranit Dror and Maxim Shatsky and
                 Ruth Nussinov and Haim J. Wolfson",
  title =        "{EMatch}: Discovery of High Resolution Structural
                 Homologues of Protein Domains in Intermediate
                 Resolution Cryo-{EM} Maps",
  journal =      j-TCBB,
  volume =       "4",
  number =       "1",
  pages =        "28--39",
  month =        jan,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:57:20 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Cryo-EM has become an increasingly powerful technique
                 for elucidating the structure, dynamics, and function
                 of large flexible macromolecule assemblies that cannot
                 be determined at atomic resolution. However, due to the
                 relatively low resolution of cryo-EM data, a major
                 challenge is to identify components of complexes
                 appearing in cryo-EM maps. Here, we describe EMatch, a
                 novel integrated approach for recognizing structural
                 homologues of protein domains present in a 6-10{\AA}
                 resolution cryo-EM map and constructing a quasi-atomic
                 structural model of their assembly. The method is
                 highly efficient and has been successfully validated on
                 various simulated data. The strength of the method is
                 demonstrated by a domain assembly of an experimental
                 cryo-EM map of native GroEL at 6{\AA} resolution.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "3D alignment of secondary structures; cyclic symmetry;
                 intermediate resolution cryo-EM maps; macromolecular
                 assemblies; structural bioinformatics",
}

@Article{Wang:2007:ACC,
  author =       "Lipo Wang and Feng Chu and Wei Xie",
  title =        "Accurate Cancer Classification Using Expressions of
                 Very Few Genes",
  journal =      j-TCBB,
  volume =       "4",
  number =       "1",
  pages =        "40--53",
  month =        jan,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:57:20 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "We aim at finding the smallest set of genes that can
                 ensure highly accurate classification of cancers from
                 microarray data by using supervised machine learning
                 algorithms. The significance of finding the minimum
                 gene subsets is three-fold: (1) It greatly reduces the
                 computational burden and `noise' arising from
                 irrelevant genes. In the examples studied in this
                 paper, finding the minimum gene subsets even allows for
                 extraction of simple diagnostic rules which lead to
                 accurate diagnosis without the need for any
                 classifiers. (2) It simplifies gene expression tests to
                 include only a very small number of genes rather than
                 thousands of genes, which can bring down the cost for
                 cancer testing significantly. (3) It calls for further
                 investigation into the possible biological relationship
                 between these small numbers of genes and cancer
                 development and treatment. Our simple yet very
                 effective method involves two steps. In the first step,
                 we choose some important genes using a feature
                 importance ranking scheme. In the second step, we test
                 the classification capability of all simple
                 combinations of those important genes by using a good
                 classifier. For three `small' and `simple' data sets
                 with two, three, and four cancer (sub)types, our
                 approach obtained very high accuracy with only two or
                 three genes. For a `large' and `complex' data set with
                 14 cancer types, we divided the whole problem into a
                 group of binary classification problems and applied the
                 2--step approach to each of these binary classification
                 problems. Through this `divide-and-conquer' approach,
                 we obtained accuracy comparable to previously reported
                 results but with only 28 genes rather than 16,063
                 genes. In general, our method can significantly reduce
                 the number of genes required for highly reliable
                 diagnosis.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "cancer classification; fuzzy; gene expression; neural
                 networks; support vector machines.",
}

@Article{Zhi:2007:CBA,
  author =       "Degui Zhi and Uri Keich and Pavel Pevzner and Steffen
                 Heber and Haixu Tang",
  title =        "Correcting Base-Assignment Errors in Repeat Regions of
                 Shotgun Assembly",
  journal =      j-TCBB,
  volume =       "4",
  number =       "1",
  pages =        "54--64",
  month =        jan,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:57:20 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Accurate base-assignment in repeat regions of a whole
                 genome shotgun assembly is an unsolved problem. Since
                 reads in repeat regions cannot be easily attributed to
                 a unique location in the genome, current assemblers may
                 place these reads arbitrarily. As a result, the
                 base-assignment error rate in repeats is likely to be
                 much higher than that in the rest of the genome. We
                 developed an iterative algorithm, EULER-AIR, that is
                 able to correct base-assignment errors in finished
                 genome sequences in public databases. The Wolbachia
                 genome is among the best finished genomes. Using this
                 genome project as an example, we demonstrated that
                 EULER-AIR can (1) discover and correct base-assignment
                 errors, (2) provide accurate read assignments, (3)
                 utilize finishing reads for accurate base-assignment,
                 and (4) provide guidance for designing finishing
                 experiments. In the genome of Wolbachia, EULER-AIR
                 found 16 positions with ambiguous base-assignment and
                 two positions with erroneous bases. Besides Wolbachia,
                 many other genome sequencing projects have
                 significantly fewer finishing reads and, hence, are
                 likely to contain more base-assignment errors in
                 repeats. We demonstrate that EULER-AIR is a software
                 tool that can be used to find and correct
                 base-assignment errors in a genome assembly project.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "expectation maximization; finishing; fragment
                 assembly",
}

@Article{Xu:2007:MCC,
  author =       "Rui Xu and Georgios C. Anagnostopoulos and Donald C.
                 Wunsch",
  title =        "Multiclass Cancer Classification Using Semisupervised
                 Ellipsoid {ARTMAP} and Particle Swarm Optimization with
                 Gene Expression Data",
  journal =      j-TCBB,
  volume =       "4",
  number =       "1",
  pages =        "65--77",
  month =        jan,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:57:20 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "It is crucial for cancer diagnosis and treatment to
                 accurately identify the site of origin of a tumor. With
                 the emergence and rapid advancement of DNA microarray
                 technologies, constructing gene expression profiles for
                 different cancer types has already become a promising
                 means for cancer classification. In addition to
                 research on binary classification such as normal versus
                 tumor samples, which attracts numerous efforts from a
                 variety of disciplines, the discrimination of multiple
                 tumor types is also important. Meanwhile, the selection
                 of genes which are relevant to a certain cancer type
                 not only improves the performance of the classifiers,
                 but also provides molecular insights for treatment and
                 drug development. Here, we use Semisupervised Ellipsoid
                 ARTMAP (ssEAM) for multiclass cancer discrimination and
                 particle swarm optimization for informative gene
                 selection. ssEAM is a neural network architecture
                 rooted in Adaptive Resonance Theory and suitable for
                 classification tasks. ssEAM features fast, stable, and
                 finite learning and creates hyperellipsoidal clusters,
                 inducing complex nonlinear decision boundaries. PSO is
                 an evolutionary algorithm-based technique for global
                 optimization. A discrete binary version of PSO is
                 employed to indicate whether genes are chosen or not.
                 The effectiveness of ssEAM\slash PSO for multiclass
                 cancer diagnosis is demonstrated by testing it on three
                 publicly available multiple-class cancer data sets.
                 ssEAM\slash PSO achieves competitive performance on all
                 these data sets, with results comparable to or better
                 than those obtained by other classifiers.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "cancer classification; gene expression profile;
                 particle swarm optimization; semisupervised ellipsoid
                 ARTMAP",
}

@Article{Huang:2007:PPP,
  author =       "Chengbang Huang and Faruck Morcos and Simon P. Kanaan
                 and Stefan Wuchty and Danny Z. Chen and Jesus A.
                 Izaguirre",
  title =        "Predicting Protein-Protein Interactions from Protein
                 Domains Using a Set Cover Approach",
  journal =      j-TCBB,
  volume =       "4",
  number =       "1",
  pages =        "78--87",
  month =        jan,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:57:20 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "One goal of contemporary proteome research is the
                 elucidation of cellular protein interactions. Based on
                 currently available protein-protein interaction and
                 domain data, we introduce a novel method, Maximum
                 Specificity Set Cover (MSSC), for the prediction of
                 protein-protein interactions. In our approach, we map
                 the relationship between interactions of proteins and
                 their corresponding domain architectures to a
                 generalized weighted set cover problem. The application
                 of a greedy algorithm provides sets of domain
                 interactions which explain the presence of protein
                 interactions to the largest degree of specificity.
                 Utilizing domain and protein interaction data of {\em
                 S. cerevisiae}, MSSC enables prediction of previously
                 unknown protein interactions, links that are well
                 supported by a high tendency of coexpression and
                 functional homogeneity of the corresponding proteins.
                 Focusing on concrete examples, we show that MSSC
                 reliably predicts protein interactions in well-studied
                 molecular systems, such as the 26S proteasome and RNA
                 polymerase II of {\em S. cerevisiae}. We also show that
                 the quality of the predictions is comparable to the
                 Maximum Likelihood Estimation while MSSC is faster.
                 This new algorithm and all data sets used are
                 accessible through a Web portal at
                 \path=http://ppi.cse.nd.edu=.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "bioinformatics (genome or protein) databases; biology;
                 Computations on discrete structures; genetics; graph
                 algorithms",
}

@Article{Kim:2007:AAD,
  author =       "Jong Hyun Kim and Michael S. Waterman and Lei M. Li",
  title =        "Accuracy Assessment of Diploid Consensus Sequences",
  journal =      j-TCBB,
  volume =       "4",
  number =       "1",
  pages =        "88--97",
  month =        jan,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:57:20 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "If the origins of fragments are known in genome
                 sequencing projects, it is straightforward to
                 reconstruct diploid consensus sequences. In reality,
                 however, this is not true. Although there are proposed
                 methods to reconstruct haplotypes from genome
                 sequencing projects, an accuracy assessment is required
                 to evaluate the confidence of the estimated diploid
                 consensus sequences. In this paper, we define the
                 confidence score of diploid consensus sequences. It
                 requires the calculation of the likelihood of an
                 assembly. To calculate the likelihood, we propose a
                 linear time algorithm with respect to the number of
                 polymorphic sites. The likelihood calculation and
                 confidence score are used for further improvements of
                 haplotype estimation in two directions. One direction
                 is that low-scored phases are disconnected. The other
                 direction is that, instead of using nominal frequency
                 1/2, the haplotype frequency is estimated to reflect
                 the actual contribution of each haplotype. Our method
                 was evaluated on the simulated data whose polymorphism
                 rate (1.2 percent) was based on Ciona intestinalis. As
                 a result, the high accuracy of our algorithm was
                 indicated: The true positive rate of the haplotype
                 estimation was greater than 97 percent.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "diploid; haplotype; polymorphism; shotgun sequencing",
}

@Article{Alekseyev:2007:CBG,
  author =       "Max A. Alekseyev and Pavel A. Pevzner",
  title =        "Colored {de Bruijn} Graphs and the Genome Halving
                 Problem",
  journal =      j-TCBB,
  volume =       "4",
  number =       "1",
  pages =        "98--107",
  month =        jan,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:57:20 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Breakpoint graph analysis is a key algorithmic
                 technique in studies of genome rearrangements. However,
                 breakpoint graphs are defined only for genomes without
                 duplicated genes, thus limiting their applications in
                 rearrangement analysis. We discuss a connection between
                 the breakpoint graphs and de Bruijn graphs that leads
                 to a generalization of the notion of breakpoint graph
                 for genomes with duplicated genes. We further use the
                 generalized breakpoint graphs to study the Genome
                 Halving Problem (first introduced and solved by Nadia
                 El-Mabrouk and David Sankoff). The El-Mabrouk-Sankoff
                 algorithm is rather complex, and, in this paper, we
                 present an alternative approach that is based on
                 generalized breakpoint graphs. The generalized
                 breakpoint graphs make the El-Mabrouk-Sankoff result
                 more transparent and promise to be useful in future
                 studies of genome rearrangements.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "breakpoint graph; de Bruijn graph; genome duplication;
                 genome halving; genome rearrangement; reversal",
}

@Article{Mossel:2007:DMT,
  author =       "Elchanan Mossel",
  title =        "Distorted Metrics on Trees and Phylogenetic Forests",
  journal =      j-TCBB,
  volume =       "4",
  number =       "1",
  pages =        "108--116",
  month =        jan,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:57:20 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "We study distorted metrics on binary trees in the
                 context of phylogenetic reconstruction. Given a binary
                 tree $T$ on $n$ leaves with a path metric $d$, consider
                 the pairwise distances $ d(u, v) $ between leaves. It
                 is well known that these determine the tree and the
                 $d$-length of all edges. Here, we consider distortions
                 $ \hat {d} $ of $d$ such that, for all leaves $u$ and
                 $v$, it holds that $ |d(u, v) - \hat {d}(u, v)| < f / 2
                 $ if either $ d(u, v) < M + f / 2 $ or $ \hat {d}(u, v)
                 < M + f / 2 $, where $d$ satisfies $ f \leq d(e) \leq g
                 $ for all edges $e$. Given such distortions, we show
                 how to reconstruct in polynomial time a forest $ T_1,
                 \ldots {}, T_\alpha $ such that the true tree $T$ may
                 be obtained from that forest by adding $ \alpha - 1 $
                 edges and $ \alpha - 1 \leq 2 - \Omega (M / g) n $. Our
                 distorted metric result implies a reconstruction
                 algorithm of phylogenetic forests with a small number
                 of trees from sequences of length logarithmic in the
                 number of species. The reconstruction algorithm is
                 applicable for the general Markov model. Both the
                 distorted metric result and its applications to
                 phylogeny are almost tight.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "CFN; distortion; forest; Jukes--Cantor; metric;
                 phylogenetics; tree",
}

@Article{Aeling:2007:DDE,
  author =       "Kimberly A. Aeling and Nicholas R. Steffen and Matthew
                 Johnson and G. Wesley Hatfield and Richard H. Lathrop
                 and Donald F. Senear",
  title =        "{DNA} Deformation Energy as an Indirect Recognition
                 Mechanism in Protein-{DNA} Interactions",
  journal =      j-TCBB,
  volume =       "4",
  number =       "1",
  pages =        "117--125",
  month =        jan,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:57:20 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Proteins that bind to specific locations in genomic
                 DNA control many basic cellular functions. Proteins
                 detect their binding sites using both direct and
                 indirect recognition mechanisms. Deformation energy,
                 which models the energy required to bend DNA from its
                 native shape to its shape when bound to a protein, has
                 been shown to be an indirect recognition mechanism for
                 one particular protein, Integration Host Factor (IHF).
                 This work extends the analysis of deformation to two
                 other DNA-binding proteins, CRP and SRF, and two
                 endonucleases, I-CreI and I-PpoI. Known binding sites
                 for all five proteins showed statistically significant
                 differences in mean deformation energy as compared to
                 random sequences. Binding sites for the three
                 DNA-binding proteins and one of the endonucleases had
                 mean deformation energies lower than random sequences.
                 Binding sites for I-PpoI had mean deformation energy
                 higher than random sequences. Classifiers that were
                 trained using the deformation energy at each base pair
                 step showed good cross-validated accuracy when
                 classifying unseen sequences as binders or nonbinders.
                 These results support DNA deformation energy as an
                 indirect recognition mechanism across a wider range of
                 DNA-binding proteins. Deformation energy may also have
                 a predictive capacity for the underlying catalytic
                 mechanism of DNA-binding enzymes.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "deformation energy; DNA bending; DNA-protein binding;
                 indirect readout; indirect recognition; perceptron
                 learning",
}

@Article{Yang:2007:MFE,
  author =       "Jing Yang and Sarawan Wongsa and Visakan
                 Kadirkamanathan and Stephen A. Billings and Phillip C.
                 Wright",
  title =        "Metabolic Flux Estimation-{A} Self-Adaptive
                 Evolutionary Algorithm with Singular Value
                 Decomposition",
  journal =      j-TCBB,
  volume =       "4",
  number =       "1",
  pages =        "126--138",
  month =        jan,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:57:20 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Metabolic flux analysis is important for metabolic
                 system regulation and intracellular pathway
                 identification. A popular approach for intracellular
                 flux estimation involves using $^{13}{\rm C}$ tracer
                 experiments to label states that can be measured by
                 nuclear magnetic resonance spectrometry or gas
                 chromatography mass spectrometry. However, the bilinear
                 balance equations derived from $^{13}{\rm C}$ tracer
                 experiments and the noisy measurements require a
                 nonlinear optimization approach to obtain the optimal
                 solution. In this paper, the flux quantification
                 problem is formulated as an error-minimization problem
                 with equality and inequality constraints through the
                 $^{13}{\rm C}$ balance and stoichiometric equations.
                 The stoichiometric constraints are transformed to a
                 null space by singular value decomposition.
                 Self-adaptive evolutionary algorithms are then
                 introduced for flux quantification. The performance of
                 the evolutionary algorithm is compared with ordinary
                 least squares estimation by the simulation of the
                 central pentose phosphate pathway. The proposed
                 algorithm is also applied to the central metabolism of
                 Corynebacterium glutamicum under lysine-producing
                 conditions. A comparison between the results from the
                 proposed algorithm and data from the literature is
                 given. The complexity of a metabolic system with
                 bidirectional reactions is also investigated by
                 analyzing the fluctuations in the flux estimates when
                 available measurements are varied.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "evolutionary computing; least squares method;
                 metabolic flux analysis; singular value
                 decomposition.",
}

@Article{Wu:2007:QBP,
  author =       "Gang Wu and Jia-Huai You and Guohui Lin",
  title =        "Quartet-Based Phylogeny Reconstruction with Answer Set
                 Programming",
  journal =      j-TCBB,
  volume =       "4",
  number =       "1",
  pages =        "139--152",
  month =        jan,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:57:20 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "In this paper, a new representation is presented for
                 the Maximum Quartet Consistency (MQC) problem, where
                 solving the MQC problem becomes searching for an
                 ultrametric matrix that satisfies a maximum number of
                 given quartet topologies. A number of structural
                 properties of the MQC problem in this new
                 representation are characterized through formulating
                 into answer set programming, a recent powerful logic
                 programming tool for modeling and solving search
                 problems. Using these properties, a number of
                 optimization techniques are proposed to speed up the
                 search process. The experimental results on a number of
                 simulated data sets suggest that the new
                 representation, combined with answer set programming,
                 presents a unique perspective to the MQC problem.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "Answer Set Programming (ASP); Maximum Quartet
                 Consistency (MQC); phylogeny; quartet; ultrametric
                 matrix.",
}

@Article{Reinert:2007:LLE,
  author =       "Gesine Reinert and Michael S. Waterman",
  title =        "On the Length of the Longest Exact Position Match in a
                 Random Sequence",
  journal =      j-TCBB,
  volume =       "4",
  number =       "1",
  pages =        "153--156",
  month =        jan,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:57:20 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "A mixed Poisson approximation and a Poisson
                 approximation for the length of the longest exact match
                 of a random sequence across another sequence are
                 provided, where the match is required to start at
                 position 1 in the first sequence. This problem arises
                 when looking for suitable anchors in whole genome
                 alignments.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "Chen-Stein method; length of longest match; mixed
                 Poisson approximation; Poisson approximation",
}

@Article{Au:2007:CAC,
  author =       "Wai-Ho Au and Keith C. C. Chan and Andrew K. C. Wong
                 and Yang Wang",
  title =        "Correction to {``Attribute Clustering for Grouping,
                 Selection, and Classification of Gene Expression
                 Data''}",
  journal =      j-TCBB,
  volume =       "4",
  number =       "1",
  pages =        "157--157",
  month =        jan,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:57:20 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "This is a correction to a typographical error in (11)
                 in [1] which present the calculation of the sum of the
                 multiple significant interdependence redundancy
                 measure. Equation (11) in [1] should be: $$ k = \arg
                 \max \nolimits_{k \in \{ 2, \ldots, p \} } \sum_{r =
                 1}^k \sum_{A_i \in \{ C_r - \eta_r \} }R(A_i : \eta_r).
                 $$ (11)We remark that the experimental results reported
                 in [1] are based on (11) above not (11) in [1].",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Staff:2007:RL,
  author =       "{IEEE and ACM Transactions on Computational Biology
                 and Bioinformatics staff}",
  title =        "2006 Reviewers List",
  journal =      j-TCBB,
  volume =       "4",
  number =       "1",
  pages =        "158--160",
  month =        jan,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:57:20 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Rajapakse:2007:GEI,
  author =       "Jagath C. Rajapakse and Yan-Qing Zhang and Gary B.
                 Fogel",
  title =        "{Guest Editors}' Introduction to the {Special Section:
                 Computational Intelligence Approaches in Computational
                 Biology and Bioinformatics}",
  journal =      j-TCBB,
  volume =       "4",
  number =       "2",
  pages =        "161--162",
  month =        apr,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:57:55 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Wang:2007:PBS,
  author =       "Haiying Wang and Huiru Zheng and Francisco Azuaje",
  title =        "{Poisson}-Based Self-Organizing Feature Maps and
                 Hierarchical Clustering for Serial Analysis of Gene
                 Expression Data",
  journal =      j-TCBB,
  volume =       "4",
  number =       "2",
  pages =        "163--175",
  month =        apr,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:57:55 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Serial analysis of gene expression (SAGE) is a
                 powerful technique for global gene expression
                 profiling, allowing simultaneous analysis of thousands
                 of transcripts without prior structural and functional
                 knowledge. Pattern discovery and visualization have
                 become fundamental approaches to analyzing such
                 large-scale gene expression data. From the pattern
                 discovery perspective, clustering techniques have
                 received great attention. However, due to the
                 statistical nature of SAGE data (i.e., underlying
                 distribution), traditional clustering techniques may
                 not be suitable for SAGE data analysis. Based on the
                 adaptation and improvement of Self-Organizing Maps and
                 hierarchical clustering techniques, this paper presents
                 two new clustering algorithms, namely, PoissonS and
                 PoissonHC, for SAGE data analysis. Tested on synthetic
                 and experimental SAGE data, these algorithms
                 demonstrate several advantages over traditional pattern
                 discovery techniques. The results indicate that, by
                 incorporating statistical properties of SAGE data,
                 PoissonS and PoissonHC, as well as a hybrid approach
                 (neuro-hierarchical approach) based on the combination
                 of PoissonS and PoissonHC, offer significant
                 improvements in pattern discovery and visualization for
                 SAGE data. Moreover, a user-friendly platform, which
                 may improve and accelerate SAGE data mining, was
                 implemented. The system is freely available on request
                 from the authors for nonprofit use.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "hybrid machine learning; Pattern discovery and
                 visualization; Poisson distribution; self-organizing
                 maps; serial analysis of gene expression.",
}

@Article{Sjahputera:2007:RAC,
  author =       "Ozy Sjahputera and James M. Keller and J. Wade Davis
                 and Kristen H. Taylor and Farahnaz Rahmatpanah and
                 Huidong Shi and Derek T. Anderson and Samuel N. Blisard
                 and Robert H. Luke and Mihail Popescu and Gerald C.
                 Arthur and Charles W. Caldwell",
  title =        "Relational Analysis of {CpG} Islands Methylation and
                 Gene Expression in Human Lymphomas Using Possibilistic
                 {C}-Means Clustering and Modified Cluster Fuzzy
                 Density",
  journal =      j-TCBB,
  volume =       "4",
  number =       "2",
  pages =        "176--189",
  month =        apr,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:57:55 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Heterogeneous genetic and epigenetic alterations are
                 commonly found in human non-Hodgkin's lymphomas (NHL).
                 One such epigenetic alteration is aberrant methylation
                 of gene promoter-related CpG islands, where
                 hypermethylation frequently results in transcriptional
                 inactivation of target genes, while a decrease or loss
                 of promoter methylation (hypomethylation) is frequently
                 associated with transcriptional activation. Discovering
                 genes with these relationships in NHL or other types of
                 cancers could lead to a better understanding of the
                 pathobiology of these diseases. The simultaneous
                 analysis of promoter methylation using Differential
                 Methylation Hybridization (DMH) and its associated gene
                 expression using Expressed CpG Island Sequence Tag
                 (ECIST) microarrays generates a large volume of
                 methylation-expression relational data. To analyze this
                 data, we propose a set of algorithms based on fuzzy
                 sets theory, in particular Possibilistic c-Means (PCM)
                 and cluster fuzzy density. For each gene, these
                 algorithms calculate measures of confidence of various
                 methylation-expression relationships in each NHL
                 subclass. Thus, these tools can be used as a means of
                 high volume data exploration to better guide biological
                 confirmation using independent molecular biology
                 methods.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "cluster density; clustering; expression; fuzzy sets;
                 Methylation; microarray",
}

@Article{Lu:2007:ISL,
  author =       "Yijuan Lu and Qi Tian and Feng Liu and Maribel Sanchez
                 and Yufeng Wang",
  title =        "Interactive Semisupervised Learning for Microarray
                 Analysis",
  journal =      j-TCBB,
  volume =       "4",
  number =       "2",
  pages =        "190--203",
  month =        apr,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:57:55 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Microarray technology has generated vast amounts of
                 gene expression data with distinct patterns. Based on
                 the premise that genes of correlated functions tend to
                 exhibit similar expression patterns, various machine
                 learning methods have been applied to capture these
                 specific patterns in microarray data. However, the
                 discrepancy between the rich expression profiles and
                 the limited knowledge of gene functions has been a
                 major hurdle to the understanding of cellular networks.
                 To bridge this gap so as to properly comprehend and
                 interpret expression data, we introduce Relevance
                 Feedback to microarray analysis and propose an
                 interactive learning framework to incorporate the
                 expert knowledge into the decision module. In order to
                 find a good learning method and solve two intrinsic
                 problems in microarray data, high dimensionality and
                 small sample size, we also propose a semisupervised
                 learning algorithm: Kernel Discriminant-EM (KDEM). This
                 algorithm efficiently utilizes a large set of unlabeled
                 data to compensate for the insufficiency of a small set
                 of labeled data and it extends the linear algorithm in
                 Discriminant-EM (DEM) to a kernel algorithm to handle
                 nonlinearly separable data in a lower dimensional
                 space. The Relevance Feedback technique and KDEM
                 together construct an efficient and effective
                 interactive semisupervised learning framework for
                 microarray analysis. Extensive experiments on the yeast
                 cell cycle regulation data set and Plasmodium
                 falciparum red blood cell cycle data set show the
                 promise of this approach.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "kernel DEM; microarray analysis; relevance feedback;
                 semisupervised learning",
}

@Article{Lerner:2007:CSI,
  author =       "Boaz Lerner and Josepha Yeshaya and Lev Koushnir",
  title =        "On the Classification of a Small Imbalanced
                 Cytogenetic Image Database",
  journal =      j-TCBB,
  volume =       "4",
  number =       "2",
  pages =        "204--215",
  month =        apr,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:57:55 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Solving a multiclass classification task using a small
                 imbalanced database of patterns of high dimension is
                 difficult due to the curse-of-dimensionality and the
                 bias of the training toward the majority classes. Such
                 a problem has arisen while diagnosing genetic
                 abnormalities by classifying a small database of
                 fluorescence in situ hybridization signals of types
                 having different frequencies of occurrence. We propose
                 and experimentally study using the cytogenetic domain
                 two solutions to the problem. The first is hierarchical
                 decomposition of the classification task, where each
                 hierarchy level is designed to tackle a simpler problem
                 which is represented by classes that are approximately
                 balanced. The second solution is balancing the data by
                 up-sampling the minority classes accompanied by
                 dimensionality reduction. Implemented by the naive
                 Bayesian classifier or the multilayer perceptron neural
                 network, both solutions have diminished the problem and
                 contributed to accuracy improvement. In addition, the
                 experiments suggest that coping with the smallness of
                 the data is more beneficial than dealing with its
                 imbalance.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "classification; dimensionality reduction; genetic
                 diagnosis; imbalanced data; multilayer perceptron
                 (MLP); naive Bayesian classifier (NBC); small sample
                 size.",
}

@Article{Igel:2007:GBO,
  author =       "Christian Igel and Tobias Glasmachers and Britta
                 Mersch and Nico Pfeifer and Peter Meinicke",
  title =        "Gradient-Based Optimization of Kernel-Target Alignment
                 for Sequence Kernels Applied to Bacterial Gene Start
                 Detection",
  journal =      j-TCBB,
  volume =       "4",
  number =       "2",
  pages =        "216--226",
  month =        apr,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:57:55 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Biological data mining using kernel methods can be
                 improved by a task-specific choice of the kernel
                 function. Oligo kernels for genomic sequence analysis
                 have proven to have a high discriminative power and to
                 provide interpretable results. Oligo kernels that
                 consider subsequences of different lengths can be
                 combined and parameterized to increase their
                 flexibility. For adapting these parameters efficiently,
                 gradient-based optimization of the kernel-target
                 alignment is proposed. The power of this new, general
                 model selection procedure and the benefits of fitting
                 kernels to problem classes are demonstrated by adapting
                 oligo kernels for bacterial gene start detection.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "kernel target alignment; model selection; oligo
                 kernel; sequence analysis; support vector machines;
                 translation initiation sites",
}

@Article{Ogul:2007:SLP,
  author =       "Hasan Ogul and Erkan U. Mumcuo{\u{g}}lu",
  title =        "Subcellular Localization Prediction with New Protein
                 Encoding Schemes",
  journal =      j-TCBB,
  volume =       "4",
  number =       "2",
  pages =        "227--232",
  month =        apr,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:57:55 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Subcellular localization is one of the key properties
                 in functional annotation of proteins. Support vector
                 machines (SVMs) have been widely used for automated
                 prediction of subcellular localizations. Existing
                 methods differ in the protein encoding schemes used. In
                 this study, we present two methods for protein encoding
                 to be used for SVM-based subcellular localization
                 prediction: n{\hbox{-}}\rm peptide compositions with
                 reduced amino acid alphabets for larger values of $n$
                 and pairwise sequence similarity scores based on whole
                 sequence and N-terminal sequence. We tested the methods
                 on a common benchmarking data set that consists of
                 2,427 eukaryotic proteins with four localization sites.
                 As a result of 5-fold cross-validation tests, the
                 encoding with n{\hbox{-}}\rm peptide compositions
                 provided the accuracies of 84.5, 88.9, 66.3, and 94.3
                 percent for cytoplasmic, extracellular, mitochondrial,
                 and nuclear proteins, where the overall accuracy was
                 87.1 percent. The second method provided 83.6, 87.7,
                 87.9, and 90.5 percent accuracies for individual
                 locations and 87.8 percent overall accuracy. A hybrid
                 system, which we called PredLOC, makes a final decision
                 based on the results of the two presented methods which
                 achieved an overall accuracy of 91.3 percent, which is
                 better than the achievements of many of the existing
                 methods. The new system also outperformed the recent
                 methods in the experiments conducted on a new-unique
                 SWISSPROT test set.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "n{\hbox{-}}\rm peptide composition; probabilistic
                 suffix tree; subcellular localization; support vector
                 machines.",
}

@Article{Li:2007:DSD,
  author =       "Wenyuan Li and Ying Liu and Hung-Chung Huang and
                 Yanxiong Peng and Yongjing Lin and Wee-Keong Ng and
                 Kok-Leong Ong",
  title =        "Dynamical Systems for Discovering Protein Complexes
                 and Functional Modules from Biological Networks",
  journal =      j-TCBB,
  volume =       "4",
  number =       "2",
  pages =        "233--250",
  month =        apr,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:57:55 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Recent advances in high throughput experiments and
                 annotations via published literature have provided a
                 wealth of interaction maps of several biomolecular
                 networks, including metabolic, protein-protein, and
                 protein-DNA interaction networks. The architecture of
                 these molecular networks reveals important principles
                 of cellular organization and molecular functions.
                 Analyzing such networks, i.e., discovering dense
                 regions in the network, is an important way to identify
                 protein complexes and functional modules. This task has
                 been formulated as the problem of finding heavy
                 subgraphs, the Heaviest k{\hbox{-}}\rm Subgraph Problem
                 (k{\hbox{-}}\rm HSP), which itself is NP-hard. However,
                 any method based on the k{\hbox{-}}\rm HSP requires the
                 parameter $k$ and an exact solution of k{\hbox{-}}\rm
                 HSP may still end up as a `spurious' heavy subgraph,
                 thus reducing its practicability in analyzing large
                 scale biological networks. We proposed a new
                 formulation, called the rank-HSP, and two dynamical
                 systems to approximate its results. In addition, a
                 novel metric, called the Standard deviation and Mean
                 Ratio (SMR), is proposed for use in `spurious' heavy
                 subgraphs to automate the discovery by setting a fixed
                 threshold. Empirical results on both the simulated
                 graphs and biological networks have demonstrated the
                 efficiency and effectiveness of our proposal.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "bioinformatics databases; evolutionary computing;
                 Graph algorithms; neural nets",
}

@Article{Hu:2007:DMP,
  author =       "Xiaohua Hu and Daniel D. Wu",
  title =        "Data Mining and Predictive Modeling of Biomolecular
                 Network from Biomedical Literature Databases",
  journal =      j-TCBB,
  volume =       "4",
  number =       "2",
  pages =        "251--263",
  month =        apr,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:57:55 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "In this paper, we present a novel approach Bio-IEDM
                 (Biomedical Information Extraction and Data Mining) to
                 integrate text mining and predictive modeling to
                 analyze biomolecular network from biomedical literature
                 databases. Our method consists of two phases. In phase
                 1, we discuss a semisupervised efficient learning
                 approach to automatically extract biological
                 relationships such as protein-protein interaction,
                 protein-gene interaction from the biomedical literature
                 databases to construct the biomolecular network. Our
                 method automatically learns the patterns based on a few
                 user seed tuples and then extracts new tuples from the
                 biomedical literature based on the discovered patterns.
                 The derived biomolecular network forms a large
                 scale-free network graph. In phase 2, we present a
                 novel clustering algorithm to analyze the biomolecular
                 network graph to identify biologically meaningful
                 subnetworks (communities). The clustering algorithm
                 considers the characteristics of the scale-free network
                 graphs and is based on the local density of the vertex
                 and its neighborhood functions that can be used to find
                 more meaningful clusters with different density level.
                 The experimental results indicate our approach is very
                 effective in extracting biological knowledge from a
                 huge collection of biomedical literature. The
                 integration of data mining and information extraction
                 provides a promising direction for analyzing the
                 biomolecular network.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "biological complexes (communities); biomolecular
                 network; information extraction; scale-free network;
                 semisupervised learning",
}

@Article{Neri:2007:AMA,
  author =       "Ferrante Neri and Jari Toivanen and Giuseppe Leonardo
                 Cascella and Yew-Soon Ong",
  title =        "An Adaptive Multimeme Algorithm for Designing {HIV}
                 Multidrug Therapies",
  journal =      j-TCBB,
  volume =       "4",
  number =       "2",
  pages =        "264--278",
  month =        apr,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:57:55 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "This paper proposes a period representation for
                 modeling the multidrug HIV therapies and an Adaptive
                 Multimeme Algorithm (AMmA) for designing the optimal
                 therapy. The period representation offers benefits in
                 terms of flexibility and reduction in dimensionality
                 compared to the binary representation. The AMmA is a
                 memetic algorithm which employs a list of three local
                 searchers adaptively activated by an evolutionary
                 framework. These local searchers, having different
                 features according to the exploration logic and the
                 pivot rule, have the role of exploring the decision
                 space from different and complementary perspectives
                 and, thus, assisting the standard evolutionary
                 operators in the optimization process. Furthermore, the
                 AMmA makes use of an adaptation which dynamically sets
                 the algorithmic parameters in order to prevent
                 stagnation and premature convergence. The numerical
                 results demonstrate that the application of the
                 proposed algorithm leads to very efficient medication
                 schedules which quickly stimulate a strong immune
                 response to HIV. The earlier termination of the
                 medication schedule leads to lesser unpleasant side
                 effects for the patient due to strong antiretroviral
                 therapy. A numerical comparison shows that the AMmA is
                 more efficient than three popular metaheuristics.
                 Finally, a statistical test based on the calculation of
                 the tolerance interval confirms the superiority of the
                 AMmA compared to the other methods for the problem
                 under study.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "adaptive algorithms; HIV therapy design; memetic
                 algorithms; nonlinear integer programming.",
}

@Article{Handl:2007:MOB,
  author =       "Julia Handl and Douglas B. Kell and Joshua Knowles",
  title =        "Multiobjective Optimization in Bioinformatics and
                 Computational Biology",
  journal =      j-TCBB,
  volume =       "4",
  number =       "2",
  pages =        "279--292",
  month =        apr,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:57:55 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "This paper reviews the application of multiobjective
                 optimization in the fields of bioinformatics and
                 computational biology. A survey of existing work,
                 organized by application area, forms the main body of
                 the review, following an introduction to the key
                 concepts in multiobjective optimization. An original
                 contribution of the review is the identification of
                 five distinct `contexts,' giving rise to multiple
                 objectives: These are used to explain the reasons
                 behind the use of multiobjective optimization in each
                 application area and also to point the way to potential
                 future uses of the technique.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "bioinformatics (genome or protein) databases;
                 classification and association rules; clustering;
                 experimental design; global optimization; interactive
                 data exploration and discovery; machine learning",
}

@Article{Bontempi:2007:BSI,
  author =       "Gianluca Bontempi",
  title =        "A Blocking Strategy to Improve Gene Selection for
                 Classification of Gene Expression Data",
  journal =      j-TCBB,
  volume =       "4",
  number =       "2",
  pages =        "293--300",
  month =        apr,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:57:55 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Because of high dimensionality, machine learning
                 algorithms typically rely on feature selection
                 techniques in order to perform effective classification
                 in microarray gene expression data sets. However, the
                 large number of features compared to the number of
                 samples makes the task of feature selection
                 computationally hard and prone to errors. This paper
                 interprets feature selection as a task of stochastic
                 optimization, where the goal is to select among an
                 exponential number of alternative gene subsets the one
                 expected to return the highest generalization in
                 classification. Blocking is an experimental design
                 strategy which produces similar experimental conditions
                 to compare alternative stochastic configurations in
                 order to be confident that observed differences in
                 accuracy are due to actual differences rather than to
                 fluctuations and noise effects. We propose an original
                 blocking strategy for improving feature selection which
                 aggregates in a paired way the validation outcomes of
                 several learning algorithms to assess a gene subset and
                 compare it to others. This is a novelty with respect to
                 conventional wrappers, which commonly adopt a sole
                 learning algorithm to evaluate the relevance of a given
                 set of variables. The rationale of the approach is
                 that, by increasing the amount of experimental
                 conditions under which we validate a feature subset, we
                 can lessen the problems related to the scarcity of
                 samples and consequently come up with a better
                 selection. The paper shows that the blocking strategy
                 significantly improves the performance of a
                 conventional forward selection for a set of 16 publicly
                 available cancer expression data sets. The experiments
                 involve six different classifiers and show that
                 improvements take place independent of the
                 classification algorithm used after the selection step.
                 Two further validations based on available biological
                 annotation support the claim that blocking strategies
                 in feature selection may improve the accuracy and the
                 quality of the solution. The first validation is based
                 on retrieving PubMEd abstracts associated to the
                 selected genes and matching them to regular expressions
                 describing the biological phenomenon underlying the
                 expression data sets. The biological validation that
                 follows is based on the use of the Bioconductor package
                 GoStats in order to perform Gene Ontology statistical
                 analysis.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "bioinformatics (genome or protein) databases; data
                 mining; feature evaluation and selection; machine
                 learning",
}

@Article{Diekmann:2007:EUR,
  author =       "Yoan Diekmann and Marie-France Sagot and Eric
                 Tannier",
  title =        "Evolution under Reversals: Parsimony and Conservation
                 of Common Intervals",
  journal =      j-TCBB,
  volume =       "4",
  number =       "2",
  pages =        "301--309",
  month =        apr,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:57:55 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "In comparative genomics, gene order data is often
                 modeled as signed permutations. A classical problem for
                 genome comparison is to detect common intervals in
                 permutations, that is, genes that are colocalized in
                 several species, indicating that they remained grouped
                 during evolution. A second largely studied problem
                 related to gene order is to compute a minimum scenario
                 of reversals that transforms a signed permutation into
                 another. Several studies began to mix the two problems
                 and it was observed that their results are not always
                 compatible: Often, parsimonious scenarios of reversals
                 break common intervals. If a scenario does not break
                 any common interval, it is called perfect. In two
                 recent studies, B{\'e}rard et al. defined a class of
                 permutations for which building a perfect scenario of
                 reversals sorting a permutation was achieved in
                 polynomial time and stated as an open question whether
                 it is possible to decide, given a permutation, if there
                 exists a minimum scenario of reversals that is perfect.
                 In this paper, we give a solution to this problem and
                 prove that this widens the class of permutations
                 addressed by the aforementioned studies. We implemented
                 and tested this algorithm on gene order data of
                 chromosomes from several mammal species and we compared
                 it to other methods. The algorithm helps to choose
                 among several possible scenarios of reversals and
                 indicates that the minimum scenario of reversals is not
                 always the most plausible.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "common intervals; computational biology; genome
                 rearrangements; perfect sorting; signed permutations;
                 sorting by reversals",
}

@Article{Weskamp:2007:MGA,
  author =       "Nils Weskamp and Eyke Hullermeier and Daniel Kuhn and
                 Gerhard Klebe",
  title =        "Multiple Graph Alignment for the Structural Analysis
                 of Protein Active Sites",
  journal =      j-TCBB,
  volume =       "4",
  number =       "2",
  pages =        "310--320",
  month =        apr,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:57:55 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Graphs are frequently used to describe the geometry
                 and also the physicochemical composition of protein
                 active sites. Here, the concept of graph alignment as a
                 novel method for the structural analysis of protein
                 binding pockets is presented. Using inexact
                 graph-matching techniques, one is able to identify both
                 conserved areas and regions of difference among
                 different binding pockets. Thus, using multiple graph
                 alignments, it is possible to characterize functional
                 protein families and to examine differences among
                 related protein families independent of sequence or
                 fold homology. Optimized algorithms are described for
                 the efficient calculation of multiple graph alignments
                 for the analysis of physicochemical descriptors
                 representing protein binding pockets. Additionally, it
                 is shown how the calculated graph alignments can be
                 analyzed to identify structural features that are
                 characteristic for a given protein family and also
                 features that are discriminative among related
                 families. The methods are applied to a substantial
                 high-quality subset of the PDB database and their
                 ability to successfully characterize and classify 10
                 highly populated functional protein families is shown.
                 Additionally, two related protein families from the
                 group of serine proteases are examined and important
                 structural differences are detected automatically and
                 efficiently.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "drug design; fuzzy patterns; graph mining; knowledge
                 discovery in databases; structural pattern discovery",
}

@Article{Gusfield:2007:AEAb,
  author =       "Dan Gusfield",
  title =        "{Associate Editor} Appreciation and Welcome",
  journal =      j-TCBB,
  volume =       "4",
  number =       "3",
  pages =        "321--321",
  month =        jul,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:58:24 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Fujarewicz:2007:ASM,
  author =       "Krzysztof Fujarewicz and Marek Kimmel and Tomasz
                 Lipniacki and Andrzej Swierniak",
  title =        "Adjoint Systems for Models of Cell Signaling Pathways
                 and their Application to Parameter Fitting",
  journal =      j-TCBB,
  volume =       "4",
  number =       "3",
  pages =        "322--335",
  month =        jul,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:58:24 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The paper concerns the problem of fitting mathematical
                 models of cell signaling pathways. Such models
                 frequently take the form of sets of nonlinear ordinary
                 differential equations. While the model is continuous
                 in time, the performance index used in the fitting
                 procedure, involves measurements taken at discrete time
                 moments. Adjoint sensitivity analysis is a tool, which
                 can be used for finding the gradient of a performance
                 index in the space of parameters of the model. In the
                 paper a structural formulation of adjoint sensitivity
                 analysis called the Generalized Backpropagation Through
                 Time (GBPTT) is used. The method is especially suited
                 for hybrid, continuous-discrete time systems. As an
                 example we use the mathematical model of the NF-kB
                 regulatory module, which plays a major role in the
                 innate immune response in animals.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "biology and genetics; modeling; ordinary differential
                 equations; parameter learning",
}

@Article{Wan:2007:CCN,
  author =       "Xiang Wan and Guohui Lin",
  title =        "{CISA}: Combined {NMR} Resonance Connectivity
                 Information Determination and Sequential Assignment",
  journal =      j-TCBB,
  volume =       "4",
  number =       "3",
  pages =        "336--348",
  month =        jul,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:58:24 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "A nearly complete sequential resonance assignment is a
                 key factor leading to successful protein structure
                 determination via NMR spectroscopy. Assuming the
                 availability of a set of NMR spectral peak lists, most
                 of the existing assignment algorithms first use the
                 differences between chemical shift values for common
                 nuclei across multiple spectra to provide the evidence
                 that some pairs of peaks should be assigned to
                 sequentially adjacent amino acid residues in the target
                 protein. They then use these connectivities as
                 constraints to produce a sequential assignment. At
                 various levels of success, these algorithms typically
                 generate a large number of potential connectivity
                 constraints, and it grows exponentially as the quality
                 of spectral data decreases. A key observation used in
                 our sequential assignment program, CISA, is that
                 chemical shift residual signature information can be
                 used to improve the connectivity determination, and
                 thus to dramatically decrease the number of predicted
                 connectivity constraints. Fewer connectivity
                 constraints lead to less ambiguities in the sequential
                 assignment. Extensive simulation studies on several
                 large test datasets demonstrated that CISA is efficient
                 and effective, compared to three most recently proposed
                 sequential resonance assignment programs RANDOM, PACES,
                 and MARS.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "NMR sequential resonance assignment; spin system; spin
                 system assignment; spin system residual signature; spin
                 system sequential connectivity",
}

@Article{Cameron:2007:CCS,
  author =       "Michael Cameron and Hugh Williams",
  title =        "Comparing Compressed Sequences for Faster Nucleotide
                 {BLAST} Searches",
  journal =      j-TCBB,
  volume =       "4",
  number =       "3",
  pages =        "349--364",
  month =        jul,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:58:24 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Molecular biologists, geneticists, and other life
                 scientists use the BLAST homology search package as
                 their first step for discovery of information about
                 unknown or poorly annotated genomic sequences. There
                 are two main variants of BLAST: BLASTP for searching
                 protein collections and BLASTN for nucleotide
                 collections. Surprisingly, BLASTN has had very little
                 attention; for example, the algorithms it uses do not
                 follow those described in the 1997 BLAST paper [1] and
                 no exact description has been published. It is
                 important that BLASTN is state-of-the-art: Nucleotide
                 collections such as GenBank dwarf the protein
                 collections in size, they double in size almost yearly,
                 and they take many minutes to search on modern general
                 purpose workstations. This paper proposes significant
                 improvements to the BLASTN algorithms. Each of our
                 schemes is based on compressed bytepacked formats that
                 allow queries and collection sequences to be compared
                 four bases at a time, permitting very fast query
                 evaluation using lookup tables and numeric comparisons.
                 Our most significant innovations are two new, fast
                 gapped alignment schemes that allow accurate sequence
                 alignment without decompression of the collection
                 sequences. Overall, our innovations more than double
                 the speed of BLASTN with no effect on accuracy and have
                 been integrated into our new version of BLAST that is
                 freely available for download from
                 \path=http://www.fsa-blast.org/=.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "BLAST; compression; Four Russians algorithm; homology
                 search; sequence alignment",
}

@Article{Tang:2007:DTS,
  author =       "Yuchun Tang and Yan-Qing Zhang and Zhen Huang",
  title =        "Development of Two-Stage {SVM}-{RFE} Gene Selection
                 Strategy for Microarray Expression Data Analysis",
  journal =      j-TCBB,
  volume =       "4",
  number =       "3",
  pages =        "365--381",
  month =        jul,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:58:24 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Extracting a subset of informative genes from
                 microarray expression data is a critical data
                 preparation step in cancer classification and other
                 biological function analyses. Though many algorithms
                 have been developed, the Support Vector Machine -
                 Recursive Feature Elimination (SVM-RFE) algorithm is
                 one of the best gene feature selection algorithms. It
                 assumes that a smaller `filter-out' factor in the
                 SVM-RFE, which results in a smaller number of gene
                 features eliminated in each recursion, should lead to
                 extraction of a better gene subset. Because the SVM-RFE
                 is highly sensitive to the `filter-out' factor, our
                 simulations have shown that this assumption is not
                 always correct and that the SVM-RFE is an unstable
                 algorithm. To select a set of key gene features for
                 reliable prediction of cancer types or subtypes and
                 other applications, a new two-stage SVM-RFE algorithm
                 has been developed. It is designed to effectively
                 eliminate most of the irrelevant, redundant and noisy
                 genes while keeping information loss small at the first
                 stage. A fine selection for the final gene subset is
                 then performed at the second stage. The two-stage
                 SVM-RFE overcomes the instability problem of the
                 SVM-RFE to achieve better algorithm utility. We have
                 demonstrated that the two-stage SVM-RFE is
                 significantly more accurate and more reliable than the
                 SVM-RFE and three correlation-based methods based on
                 our analysis of three publicly available microarray
                 expression datasets. Furthermore, the two-stage SVM-RFE
                 is computationally efficient because its time
                 complexity is $ O(d * \log {_2d}) $, where $d$ is the
                 size of the original gene set.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "bioinformatics; cancer classification; feature
                 selection; gene selection; microarray gene expression
                 data analysis; recursive feature elimination; support
                 vector machines",
}

@Article{Ng:2007:NGW,
  author =       "Lydia Ng and Sayan Pathak and Chihchau Kuan and Chris
                 Lau and Hong-wei Dong and Andrew Sodt and Chinh Dang
                 and Brian Avants and Paul Yushkevich and James Gee and
                 David Haynor and Ed Lein and Allan Jones and Mike
                 Hawrylycz",
  title =        "Neuroinformatics for Genome-Wide {$3$-D} Gene
                 Expression Mapping in the Mouse Brain",
  journal =      j-TCBB,
  volume =       "4",
  number =       "3",
  pages =        "382--393",
  month =        jul,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:58:24 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Large scale gene expression studies in the mammalian
                 brain offer the promise of understanding the topology,
                 networks and ultimately the function of its complex
                 anatomy, opening previously unexplored avenues in
                 neuroscience. High-throughput methods permit
                 genome-wide searches to discover genes that are
                 uniquely expressed in brain circuits and regions that
                 control behavior. Previous gene expression mapping
                 studies in model organisms have employed situ
                 hybridization (ISH), a technique that uses labeled
                 nucleic acid probes to bind to specific mRNA
                 transcripts in tissue sections. A key requirement for
                 this effort is the development of fast and robust
                 algorithms for anatomically mapping and quantifying
                 gene expression for ISH. We describe a neuroinformatics
                 pipeline for automatically mapping expression profiles
                 of ISH data and its use to produce the first genomic
                 scale 3-D mapping of gene expression in a mammalian
                 brain. The pipeline is fully automated and adaptable to
                 other organisms and tissues. Our automated study of
                 over 20,000 genes indicates that at least 78.8\% are
                 expressed at some level in the adult C56BL/6J mouse
                 brain. In addition to providing a platform for genomic
                 scale search, high-resolution images and visualization
                 tools for expression analysis are available at the
                 Allen Brain Atlas web site
                 (http://www.brain-map.org).",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "bioinformatics (genome or protein) databases; data
                 mining; information visualization; registration;
                 segmentation",
}

@Article{Nguyen:2007:RRN,
  author =       "C. Thach Nguyen and Nguyen Bao Nguyen and Wing-Kin
                 Sung and Louxin Zhang",
  title =        "Reconstructing Recombination Network from Sequence
                 Data: The Small Parsimony Problem",
  journal =      j-TCBB,
  volume =       "4",
  number =       "3",
  pages =        "394--402",
  month =        jul,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:58:24 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The small parsimony problem is studied for
                 reconstructing recombination networks from sequence
                 data. The small parsimony problem is polynomial-time
                 solvable for phylogenetic trees. However, the problem
                 is proved NP-hard even for galled recombination
                 networks. A dynamic programming algorithm is also
                 developed to solve the small parsimony problem. It
                 takes $ O(d n2^{3h}) $ time on an input recombination
                 network over length-$d$ sequences in which there are
                 $h$ recombination and $ n - h $ tree nodes.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "approximability; combination network; dynamic
                 programming; NP-hardness; parsimony method;
                 phylogenetic network",
}

@Article{Lones:2007:RMD,
  author =       "Michael Lones and Andy Tyrrell",
  title =        "Regulatory Motif Discovery Using a Population
                 Clustering Evolutionary Algorithm",
  journal =      j-TCBB,
  volume =       "4",
  number =       "3",
  pages =        "403--414",
  month =        jul,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:58:24 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "This paper describes a novel evolutionary algorithm
                 for regulatory motif discovery in DNA promoter
                 sequences. The algorithm uses data clustering to
                 logically distribute the evolving population across the
                 search space. Mating then takes place within local
                 regions of the population, promoting overall solution
                 diversity and encouraging discovery of multiple
                 solutions. Experiments using synthetic data sets have
                 demonstrated the algorithm's capacity to find position
                 frequency matrix models of known regulatory motifs in
                 relatively long promoter sequences. These experiments
                 have also shown the algorithm's ability to maintain
                 diversity during search and discover multiple motifs
                 within a single population. The utility of the
                 algorithm for discovering motifs in real biological
                 data is demonstrated by its ability to find meaningful
                 motifs within muscle-specific regulatory sequences.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "evolutionary computation; motif discovery;
                 muscle-specific gene expression; population-based data
                 clustering; transcription factor binding sites",
}

@Article{Yip:2007:SIS,
  author =       "Andy M. Yip and Michael K. Ng and Edmond H. Wu and
                 Tony F. Chan",
  title =        "Strategies for Identifying Statistically Significant
                 Dense Regions in Microarray Data",
  journal =      j-TCBB,
  volume =       "4",
  number =       "3",
  pages =        "415--429",
  month =        jul,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:58:24 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "We propose and study the notion of dense regions for
                 the analysis of categorized gene expression data and
                 present some searching algorithms for discovering them.
                 The algorithms can be applied to any categorical data
                 matrices derived from gene expression level matrices.
                 We demonstrate that dense regions are simple but useful
                 and statistically significant patterns that can be used
                 to (1) identify genes and/or samples of interest and
                 (2) eliminate genes and/or samples corresponding to
                 outliers, noise, or abnormalities. Some theoretical
                 studies on the properties of the dense regions are
                 presented which allow us to characterize dense regions
                 into several classes and to derive tailor-made
                 algorithms for different classes of regions. Moreover,
                 an empirical simulation study on the distribution of
                 the size of dense regions is carried out which is then
                 used to assess the significance of dense regions and to
                 derive effective pruning methods to speed up the
                 searching algorithms. Real microarray data sets are
                 employed to test our methods. Comparisons with six
                 other well-known clustering algorithms using synthetic
                 and real data are also conducted which confirm the
                 superiority of our methods in discovering dense
                 regions. The DRIFT code and a tutorial are available as
                 supplemental material, which can be found on the
                 Computer Society Digital Library at
                 \path=http://computer.org/tcbb/archives.htm=.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "bicluster; categorical data; clustering; coexpressed
                 genes; dense region; gene expression; microarray",
}

@Article{Liang:2007:BBD,
  author =       "Kuo-ching Liang and Xiaodong Wang and Dimitris
                 Anastassiou",
  title =        "{Bayesian} Basecalling for {DNA} Sequence Analysis
                 Using Hidden {Markov} Models",
  journal =      j-TCBB,
  volume =       "4",
  number =       "3",
  pages =        "430--440",
  month =        jul,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:58:24 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "It has been shown that electropherograms of DNA
                 sequences can be modeled with hidden Markov models.
                 Basecalling, the procedure that determines the sequence
                 of bases from the given eletropherogram, can then be
                 performed using the Viterbi algorithm. A training step
                 is required prior to basecalling in order to estimate
                 the HMM parameters. In this paper, we propose a
                 Bayesian approach which employs the Markov chain Monte
                 Carlo (MCMC) method to perform basecalling. Such an
                 approach not only allows one to naturally encode the
                 prior biological knowledge into the basecalling
                 algorithm, it also exploits both the training data and
                 the basecalling data in estimating the HMM parameters,
                 leading to more accurate estimates. Using the recently
                 sequenced genome of the organism Legionella pneumophila
                 we show that the MCMC basecaller outperforms the
                 state-of-the-art basecalling algorithm in terms of
                 total errors while requiring much less training than
                 other proposed statistical basecallers.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "basecalling; DNA sequencing; electropherogram; hidden
                 Markov model (HMM); Markov chain Monte Carlo (MCMC)",
}

@Article{Thireou:2007:BLS,
  author =       "Trias Thireou and Martin Reczko",
  title =        "Bidirectional Long Short-Term Memory Networks for
                 Predicting the Subcellular Localization of Eukaryotic
                 Proteins",
  journal =      j-TCBB,
  volume =       "4",
  number =       "3",
  pages =        "441--446",
  month =        jul,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:58:24 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "An algorithm called Bidirectional Long Short-Term
                 Memory Networks (BLSTM) for processing sequential data
                 is introduced. This supervised learning method trains a
                 special recurrent neural network to use very long
                 ranged symmetric sequence context using a combination
                 of nonlinear processing elements and linear feedback
                 loops for storing long-range context. The algorithm is
                 applied to the sequence-based prediction of protein
                 localization and predicts 93.3\% novel non-plant
                 proteins and 88.4\% novel plant proteins correctly,
                 which is an improvement over feedforward and standard
                 recurrent networks solving the same problem. The BLSTM
                 system is available as a web-service
                 (http://www.stepc.gr/~synaptic/blstm.html).",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "biological sequence analysis; long short-term memory;
                 protein subcellular localization prediction; recurrent
                 neural networks",
}

@Article{Korodi:2007:CAN,
  author =       "Gergely Korodi and Ioan Tabus",
  title =        "Compression of Annotated Nucleotide Sequences",
  journal =      j-TCBB,
  volume =       "4",
  number =       "3",
  pages =        "447--457",
  month =        jul,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:58:24 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "This article introduces an algorithm for the lossless
                 compression of DNA files, which contain annotation text
                 besides the nucleotide sequence. First a grammar is
                 specifically designed to capture the regularities of
                 the annotation text. A revertible transformation uses
                 the grammar rules in order to equivalently represent
                 the original file as a collection of parsed segments
                 and a sequence of decisions made by the grammar parser.
                 This decomposition enables the efficient use of
                 state-of-the-art encoders for processing the parsed
                 segments. The output size of the decision-making
                 process of the grammar is optimized by extending the
                 states to account for high-order Markovian
                 dependencies. The practical implementation of the
                 algorithm achieves a significant improvement when
                 compared to the general-purpose methods currently used
                 for DNA files.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "4 [Data]: Coding and Information Theory | Data
                 compaction and compression; Annotation; Compression;
                 F.4 [Theory of Computation]: Mathematical Logic and
                 Formal Languages | Formal languages; Formal Grammars;
                 G.3 [Mathematics of Computing]: Probability and
                 Statistics | Markov processes; J.3 [Computer
                 Applications]: Life and Medical Sciences | Biology and
                 genetics; nucleotide sequences",
}

@Article{Bordewich:2007:CHN,
  author =       "Magnus Bordewich and Charles Semple",
  title =        "Computing the Hybridization Number of Two Phylogenetic
                 Trees Is Fixed-Parameter Tractable",
  journal =      j-TCBB,
  volume =       "4",
  number =       "3",
  pages =        "458--466",
  month =        jul,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:58:24 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Reticulation processes in evolution mean that the
                 ancestral history of certain groups of present-day
                 species is non-tree-like. These processes include
                 hybridization, lateral gene transfer, and
                 recombination. Despite the existence of reticulation,
                 such events are relatively rare and so a fundamental
                 problem for biologists is the following: given a
                 collection of rooted binary phylogenetic trees on sets
                 of species that correctly represent the tree-like
                 evolution of different parts of their genomes, what is
                 the smallest number of `reticulation' vertices in any
                 network that explains the evolution of the species
                 under consideration. It has been previously shown that
                 this problem is NP-hard even when the collection
                 consists of only two rooted binary phylogenetic trees.
                 However, in this paper, we show that the problem is
                 fixed-parameter tractable in the two-tree instance,
                 when parameterized by this smallest number of
                 reticulation vertices.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "agreement forest; hybridization network; reticulate
                 evolution; rooted phylogenetic tree; subtree prune and
                 regraft",
}

@Article{Huang:2007:EGS,
  author =       "D. Huang and Tommy Chow",
  title =        "Effective Gene Selection Method With Small Sample Sets
                 Using Gradient-Based and Point Injection Techniques",
  journal =      j-TCBB,
  volume =       "4",
  number =       "3",
  pages =        "467--475",
  month =        jul,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:58:24 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Microarray gene expression data usually consist of a
                 large amount of genes. Among these genes, only a small
                 fraction is informative for performing cancer
                 diagnostic test. This paper focuses on effective
                 identification of informative genes. We analyze gene
                 selection models from the perspective of optimization
                 theory. As a result, a new strategy is designed to
                 modify conventional search engines. Also, as
                 overfitting is likely to occur in microarray data
                 because of their small sample set, a point injection
                 technique is developed to address the problem of
                 overfitting. The proposed strategies have been
                 evaluated on three kinds of cancer diagnosis. Our
                 results show that the proposed strategies can improve
                 the performance of gene selection substantially. The
                 experimental results also indicate that the proposed
                 methods are very robust under all the investigated
                 cases.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "gene selection; gradient based learning; optimization
                 theory; point injection",
}

@Article{Hecht:2007:HTL,
  author =       "David Hecht and Gary Fogel",
  title =        "High-Throughput Ligand Screening via Preclustering and
                 Evolved Neural Networks",
  journal =      j-TCBB,
  volume =       "4",
  number =       "3",
  pages =        "476--484",
  month =        jul,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:58:24 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The pathway for novel lead drug discovery has many
                 major deficiencies, the most significant of which is
                 the immense size of small molecule diversity space.
                 Methods that increase the search efficiency and/or
                 reduce the size of the search space, increase the rate
                 at which useful lead compounds are identified.
                 Artificial neural networks optimized via evolutionary
                 computation provide a cost and time-effective solution
                 to this problem. Here, we present results that suggest
                 preclustering of small molecules prior to neural
                 network optimization is useful for generating models of
                 quantitative structure-activity relationships for a set
                 of HIV inhibitors. Using these methods, it is possible
                 to prescreen compounds to separate active from inactive
                 compounds or even actives and mildly active compounds
                 from inactive compounds with high predictive accuracy
                 while simultaneously reducing the feature space. It is
                 also possible to identify `human interpretable'
                 features from the best models that can be used for
                 proposal and synthesis of new compounds in order to
                 optimize potency and specificity.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "artificial neural networks; computational
                 intelligence; evolutionary computation; medicine and
                 science",
}

@Article{Zhang:2007:MCU,
  author =       "Runxuan Zhang and Guang-Bin Huang and N. Sundararajan
                 and P. Saratchandran",
  title =        "Multicategory Classification Using An Extreme Learning
                 Machine for Microarray Gene Expression Cancer
                 Diagnosis",
  journal =      j-TCBB,
  volume =       "4",
  number =       "3",
  pages =        "485--495",
  month =        jul,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:58:24 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "In this paper, the recently developed Extreme Learning
                 Machine (ELM) is used for direct multicategory
                 classification problems in the cancer diagnosis area.
                 ELM avoids problems like local minima, improper
                 learning rate and overfitting commonly faced by
                 iterative learning methods and completes the training
                 very fast. We have evaluated the multi-category
                 classification performance of ELM on three benchmark
                 microarray datasets for cancer diagnosis, namely, the
                 GCM dataset, the Lung dataset and the Lymphoma dataset.
                 The results indicate that ELM produces comparable or
                 better classification accuracies with reduced training
                 time and implementation complexity compared to
                 artificial neural networks methods like conventional
                 back-propagation ANN, Linder's SANN, and Support Vector
                 Machine methods like SVM-OVO and Ramaswamy's SVM-OVA.
                 ELM also achieves better accuracies for classification
                 of individual categories.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "extreme learning machine; gene expression; microarray;
                 multi-category classification; SVM",
}

@Article{Zhang:2007:SSS,
  author =       "Louxin Zhang",
  title =        "Superiority of Spaced Seeds for Homology Search",
  journal =      j-TCBB,
  volume =       "4",
  number =       "3",
  pages =        "496--505",
  month =        jul,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:58:24 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "In homology search, good spaced seeds have higher
                 sensitivity for the same cost (weight). However,
                 elucidating the mechanism that confers power to spaced
                 seeds and characterizing optimal spaced seeds still
                 remain unsolved. This paper investigates these two
                 important open questions by formally analyzing the
                 average number of non-overlapping hits and the hit
                 probability of a spaced seed in the Bernoulli sequence
                 model. We prove that when the length of a non-uniformly
                 spaced seed is bounded above by an exponential function
                 of the seed weight, the seed outperforms strictly the
                 traditional consecutive seed of the same weight in both
                 (i) the average number of non-overlapping hits and (ii)
                 the asymptotic hit probability. This clearly answers
                 the first problem mentioned above in the Bernoulli
                 sequence model. The theoretical study in this paper
                 also gives a new solution to finding long optimal
                 seeds.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "homology search; pattern matching; renewal theory; run
                 statistics; sequence alignment; spaced seeds",
}

@Article{Matsen:2007:OCT,
  author =       "Frederick Matsen",
  title =        "Optimization Over a Class of Tree Shape Statistics",
  journal =      j-TCBB,
  volume =       "4",
  number =       "3",
  pages =        "506--512",
  month =        jul,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:58:24 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Tree shape statistics quantify some aspect of the
                 shape of a phylogenetic tree. They are commonly used to
                 compare reconstructed trees to evolutionary models and
                 to find evidence of tree reconstruction bias.
                 Historically, to find a useful tree shape statistic,
                 formulas have been invented by hand and then evaluated
                 for utility. This article presents the first method
                 which is capable of optimizing over a class of tree
                 shape statistics, called Binary Recursive Tree Shape
                 Statistics (BRTSS). After defining the BRTSS class, a
                 set of algebraic expressions is defined which can be
                 used in the recursions. The tree shape statistics
                 definable using these expressions in the BRTSS is very
                 general, and includes many of the statistics with which
                 phylogenetic researchers are already familiar. We then
                 present a practical genetic algorithm which is capable
                 of performing optimization over BRTSS given any
                 objective function. The chapter concludes with a
                 successful application of the methods to find a new
                 statistic which indicates a significant difference
                 between two distributions on trees which were
                 previously postulated to have similar properties.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "biology and genetics; evolutionary computing and
                 genetic algorithms",
}

@Article{Mandoiu:2007:GEI,
  author =       "Ion I. M{\~a}ndoiu and Yi Pan and Alexander
                 Zelikovsky",
  title =        "{Guest Editors}' Introduction to the {Special Section
                 on Bioinformatics Research and Applications}",
  journal =      j-TCBB,
  volume =       "4",
  number =       "4",
  pages =        "513--514",
  month =        oct,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:58:47 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Zheng:2007:RNA,
  author =       "Chunfang Zheng and Qian Zhu and David Sankoff",
  title =        "Removing Noise and Ambiguities from Comparative Maps
                 in Rearrangement Analysis",
  journal =      j-TCBB,
  volume =       "4",
  number =       "4",
  pages =        "515--522",
  month =        oct,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:58:47 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Comparison of genomic maps is hampered by errors and
                 ambiguities introduced by mapping technology,
                 incorrectly resolved paralogy, small samples of markers
                 and extensive genome rearrangement. We design an
                 analysis to remove or resolve most of these problems
                 and to extract corrected data where markers occur in
                 consecutive strips in both genomes. To do this we
                 introduce the notion of pre-strip, an efficient way of
                 generating these, and a compatibility analysis
                 culminating in a Maximum Weighted Clique (MWC) search.
                 The output can be directly analyzed with genome
                 rearrangement algorithms, allowing the restoration of
                 some of the data not incorporated into the clique
                 solution. We investigate the trade-off between criteria
                 for discarding excessive pre-strips to make MWC
                 feasible, in terms of retaining as many markers as
                 possible in the solution and producing an economical
                 rearrangement analysis. We explore these questions
                 through simulation and through comparison of the rice
                 and sorghum genomes.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "genome rearrangements; Maximum Weight Clique; rice;
                 sorghum; synteny blocks",
}

@Article{Blin:2007:CGD,
  author =       "Guillaume Blin and Cedric Chauve and Guillaume Fertin
                 and Romeo Rizzi and Stephane Vialette",
  title =        "Comparing Genomes with Duplications: {A} Computational
                 Complexity Point of View",
  journal =      j-TCBB,
  volume =       "4",
  number =       "4",
  pages =        "523--534",
  month =        oct,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:58:47 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "In this paper, we are interested in the computational
                 complexity of computing (dis)similarity measures
                 between two genomes when they contain duplicated genes
                 or genomic markers, a problem that happens frequently
                 when comparing whole nuclear genomes. Recently, several
                 methods ([1], [2]) have been proposed that are based on
                 two steps to compute a given (dis)similarity measure
                 $M$ between two genomes $ G_1 $ and $ G_2 $: first, one
                 establishes a one-to-one correspondence between genes
                 of $ G_1 $ and genes of $ G_2 $ ; second, once this
                 correspondence is established, it defines explicitly a
                 permutation and it is then possible to quantify their
                 similarity using classical measures defined for
                 permutations, like the number of breakpoints. Hence
                 these methods rely on two elements: a way to establish
                 a one-to-one correspondence between genes of a pair of
                 genomes, and a (dis)similarity measure for
                 permutations. The problem is then, given a
                 (dis)similarity measure for permutations, to compute a
                 correspondence that defines an optimal permutation for
                 this measure. We are interested here in two models to
                 compute a one-to-one correspondence: the exemplar
                 model, where all but one copy are deleted in both
                 genomes for each gene family, and the matching model,
                 that computes a maximal correspondence for each gene
                 family. We show that for these two models, and for
                 three (dis)similarity measures on permutations, namely
                 the number of common intervals, the maximum adjacency
                 disruption (MAD) number and the summed adjacency
                 disruption (SAD) number, the problem of computing an
                 optimal correspondence is NP-complete, and even APXhard
                 for the MAD number and SAD number.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "common intervals; comparative genomics; computational
                 complexity; maximum adjacency disruption number; summed
                 adjacency disruption number",
}

@Article{Bonizzoni:2007:ELC,
  author =       "Paola Bonizzoni and Gianluca Della Vedova and Riccardo
                 Dondi and Guillaume Fertin and Raffaella Rizzi and
                 Stephane Vialette",
  title =        "Exemplar Longest Common Subsequence",
  journal =      j-TCBB,
  volume =       "4",
  number =       "4",
  pages =        "535--543",
  month =        oct,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:58:47 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "In this paper, we investigate the computational and
                 approximation complexity of the Exemplar Longest Common
                 Subsequence of a set of sequences (ELCS problem), a
                 generalization of the Longest Common Subsequence
                 problem, where the input sequences are over the union
                 of two disjoint sets of symbols, a set of mandatory
                 symbols and a set of optional symbols. We show that
                 different versions of the problem are APX-hard even for
                 instances with two sequences. Moreover, we show that
                 the related problem of determining the existence of a
                 feasible solution of the Exemplar Longest Common
                 Subsequence of two sequences is NP-hard. On the
                 positive side, we first present an efficient algorithm
                 for the ELCS problem over instances of two sequences
                 where each mandatory symbol can appear in total at most
                 three times in the sequences. Furthermore, we present
                 two fixed-parameter algorithms for the ELCS problem
                 over instances of two sequences where the parameter is
                 the number of mandatory symbols.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "algorithm design and analysis; analysis of algorithms
                 and problem complexity; combinatorial algorithms;
                 comparative genomics; longest common subsequence",
}

@Article{Davila:2007:FPA,
  author =       "Jaime Davila and Sudha Balla and Sanguthevar
                 Rajasekaran",
  title =        "Fast and Practical Algorithms for Planted $ (l, d) $
                 Motif Search",
  journal =      j-TCBB,
  volume =       "4",
  number =       "4",
  pages =        "544--552",
  month =        oct,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:58:47 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "We consider the planted $ (l, d) $ motif search
                 problem, which consists of finding a substring of
                 length $l$ that occurs in a set of input sequences $ \{
                 s_1, \ldots {}, s_n \} $ with up to $d$ errors, a
                 problem that arises from the need to find transcription
                 factor-binding sites in genomic information. We propose
                 a sequence of practical algorithms, which start based
                 on the ideas considered in PMS1. These algorithms are
                 exact, have little space requirements, and are able to
                 tackle challenging instances with bigger $d$, taking
                 less time in the instances reported solved by exact
                 algorithms. In particular, one of the proposed
                 algorithms, PMSprune, is able to solve the challenging
                 instances, such as (17, 6) and (19, 7), which were not
                 previously reported as solved in the literature.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "branch and bound algorithms; challenging instances;
                 exact algorithms; planted motif search problem",
}

@Article{Schneider:2007:SDM,
  author =       "Adrian Schneider and Gaston Gonnet and Gina
                 Cannarozzi",
  title =        "{SynPAM---A} Distance Measure Based on Synonymous
                 Codon Substitutions",
  journal =      j-TCBB,
  volume =       "4",
  number =       "4",
  pages =        "553--560",
  month =        oct,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:58:47 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Measuring evolutionary distances between DNA or
                 protein sequences forms the basis of many applications
                 in computational biology and evolutionary studies. Of
                 particular interest are distances based on synonymous
                 substitutions, since these substitutions are considered
                 to be under very little selection pressure and
                 therefore assumed to accumulate in an almost clock-like
                 manner. SynPAM, the method presented here, allows the
                 estimation of distances between coding DNA sequences
                 based on synonymous codon substitutions. The problem of
                 estimating an accurate distance from the observed
                 substitution pattern is solved by maximum-likelihood
                 with empirical codon substitution matrices employed for
                 the underlying Markov model. Comparisons with
                 established measures of synonymous distance indicate
                 that SynPAM has less variance and yields useful results
                 over a longer time range.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "dS; evolutionary distance; molecular evolution;
                 synonymous substitutions; SynPAM",
}

@Article{Sridhar:2007:AEN,
  author =       "Srinath Sridhar and Kedar Dhamdhere and Guy Blelloch
                 and Eran Halperin and R. Ravi and Russell Schwartz",
  title =        "Algorithms for Efficient Near-Perfect Phylogenetic
                 Tree Reconstruction in Theory and Practice",
  journal =      j-TCBB,
  volume =       "4",
  number =       "4",
  pages =        "561--571",
  month =        oct,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:58:47 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "We consider the problem of reconstructing near-perfect
                 phylogenetic trees using binary character states
                 (referred to as BNPP). A perfect phylogeny assumes that
                 every character mutates at most once in the
                 evolutionary tree, yielding an algorithm for binary
                 character states that is computationally efficient but
                 not robust to imperfections in real data. A
                 near-perfect phylogeny relaxes the perfect phylogeny
                 assumption by allowing at most a constant number of
                 additional mutations. We develop two algorithms for
                 constructing optimal near-perfect phylogenies and
                 provide empirical evidence of their performance. The
                 first simple algorithm is fixed parameter tractable
                 when the number of additional mutations and the number
                 of characters that share four gametes with some other
                 character are constants. The second, more involved
                 algorithm for the problem is fixed parameter tractable
                 when only the number of additional mutations is fixed.
                 We have implemented both algorithms and shown them to
                 be extremely efficient in practice on biologically
                 significant data sets. This work proves the BNPP
                 problem fixed parameter tractable and provides the
                 first practical phylogenetic tree reconstruction
                 algorithms that find guaranteed optimal solutions while
                 being easily implemented and computationally feasible
                 for data sets of biologically meaningful size and
                 complexity.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "biology and genetics; computations on discrete
                 structures; trees",
}

@Article{Chen:2007:CBR,
  author =       "Jinmiao Chen and Narendra Chaudhari",
  title =        "Cascaded Bidirectional Recurrent Neural Networks for
                 Protein Secondary Structure Prediction",
  journal =      j-TCBB,
  volume =       "4",
  number =       "4",
  pages =        "572--582",
  month =        oct,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:58:47 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Protein secondary structure (PSS) prediction is an
                 important topic in bioinformatics. Our study on a large
                 set of non-homologous proteins shows that long-range
                 interactions commonly exist and negatively affect PSS
                 prediction. Besides, we also reveal strong correlations
                 between secondary structure (SS) elements. In order to
                 take into account the long-range interactions and SS-SS
                 correlations, we propose a novel prediction system
                 based on cascaded bidirectional recurrent neural
                 network (BRNN). We compare the cascaded BRNN against
                 another two BRNN architectures, namely the original
                 BRNN architecture used for speech recognition as well
                 as Pollastri's BRNN that was proposed for PSS
                 prediction. Our cascaded BRNN achieves an overall three
                 state accuracy Q3 of 74.38\%, and reaches a high
                 Segment OVerlap (SOV) of 66.0455. It outperforms the
                 original BRNN and Pollastri's BRNN in both Q3 and SOV.
                 Specifically, it improves the SOV score by 4-6\%.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Xiong:2007:DDK,
  author =       "Huilin Xiong and Ya Zhang and Xue-Wen Chen",
  title =        "Data-Dependent Kernel Machines for Microarray Data
                 Classification",
  journal =      j-TCBB,
  volume =       "4",
  number =       "4",
  pages =        "583--595",
  month =        oct,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:58:47 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "One important application of gene expression analysis
                 is to classify tissue samples according to their gene
                 expression levels. Gene expression data are typically
                 characterized by high dimensionality and small sample
                 size, which makes the classification task quite
                 challenging. In this paper, we present a data-dependent
                 kernel for microarray data classification. This kernel
                 function is engineered so that the class separability
                 of the training data is maximized. A
                 bootstrapping-based resampling scheme is introduced to
                 reduce the possible training bias. The effectiveness of
                 this adaptive kernel for microarray data classification
                 is illustrated with a k-Nearest Neighbor (KNN)
                 classifier. Our experimental study shows that the
                 data-dependent kernel leads to a significant
                 improvement in the accuracy of KNN classifiers.
                 Furthermore, this kernel-based KNN scheme has been
                 demonstrated to be competitive to, if not better than,
                 more sophisticated classifiers such as Support Vector
                 Machines (SVMs) and the Uncorrelated Linear
                 Discriminant Analysis (ULDA) for classifying gene
                 expression data.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "bootstrapping resampling; cancer classification;
                 kernel machines; kernel optimization; microarray data
                 analysis",
}

@Article{Michal:2007:FCM,
  author =       "Shahar Michal and Tor Ivry and Omer Cohen and Moshe
                 Sipper and Danny Barash",
  title =        "Finding a Common Motif of {RNA} Sequences Using
                 Genetic Programming: The {GeRNAMo} System",
  journal =      j-TCBB,
  volume =       "4",
  number =       "4",
  pages =        "596--610",
  month =        oct,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:58:47 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "We focus on finding a consensus motif of a set of
                 homologous or functionally related RNA molecules.
                 Recent approaches to this problem have been limited to
                 simple motifs, require sequence alignment, and make
                 prior assumptions concerning the data set. We use
                 genetic programming to predict RNA consensus motifs
                 based solely on the data set. Our system -- dubbed
                 GeRNAMo (Genetic programming of RNA Motifs) -- predicts
                 the most common motifs without sequence alignment and
                 is capable of dealing with any motif size. Our program
                 only requires the maximum number of stems in the motif,
                 and if prior knowledge is available the user can
                 specify other attributes of the motif (e.g., the range
                 of the motif's minimum and maximum sizes), thereby
                 increasing both sensitivity and speed. We describe
                 several experiments using either ferritin iron response
                 element (IRE); signal recognition particle (SRP); or
                 microRNA sequences, showing that the most common motif
                 is found repeatedly, and that our system offers
                 substantial advantages over previous methods.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{McIntosh:2007:HCR,
  author =       "Tara McIntosh and Sanjay Chawla",
  title =        "High Confidence Rule Mining for Microarray Analysis",
  journal =      j-TCBB,
  volume =       "4",
  number =       "4",
  pages =        "611--623",
  month =        oct,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:58:47 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "We present an association rule mining method for
                 mining high confidence rules, which describe
                 interesting gene relationships from microarray
                 datasets. Microarray datasets typically contain an
                 order of magnitude more genes than experiments,
                 rendering many data mining methods impractical as they
                 are optimised for sparse datasets. A new family of
                 row-enumeration rule mining algorithms have emerged to
                 facilitate mining in dense datasets. These algorithms
                 rely on pruning infrequent relationships to reduce the
                 search space by using the support measure. This major
                 shortcoming results in the pruning of many potentially
                 interesting rules with low support but high confidence.
                 We propose a new row-enumeration rule mining method,
                 MaxConf, to mine high confidence rules from microarray
                 data. MaxConf is a support-free algorithm which
                 directly uses the confidence measure to effectively
                 prune the search space. Experiments on three microarray
                 datasets show that MaxConf outperforms support-based
                 rule mining with respect to scalability and rule
                 extraction. Furthermore, detailed biological analyses
                 demonstrate the effectiveness of our approach -- the
                 rules discovered by MaxConf are substantially more
                 interesting and meaningful compared with support-based
                 methods.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "association rules; data mining; high confidence rule
                 mining; microarray analysis",
}

@Article{Ponzoni:2007:IAR,
  author =       "Ignacio Ponzoni and Francisco Azuaje and Juan Augusto
                 and David Glass",
  title =        "Inferring Adaptive Regulation Thresholds and
                 Association Rules from Gene Expression Data through
                 Combinatorial Optimization Learning",
  journal =      j-TCBB,
  volume =       "4",
  number =       "4",
  pages =        "624--634",
  month =        oct,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:58:47 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "There is a need to design computational methods to
                 support the prediction of gene regulatory networks.
                 Such models should offer both biologically-meaningful
                 and computationally-accurate predictions, which in
                 combination with other techniques may improve
                 large-scale, integrative studies. This paper presents a
                 new machine learning method for the prediction of
                 putative regulatory associations from expression data,
                 which exhibit properties never or only partially
                 addressed by other techniques recently published. The
                 method was tested on a Saccharomyces cerevisiae gene
                 expression dataset. The results were statistically
                 validated and compared with the relationships inferred
                 by two machine learning approaches to gene regulatory
                 network prediction. Furthermore, the resulting
                 predictions were assessed using domain knowledge. The
                 proposed algorithm may be able to accurately predict
                 relevant biological associations between genes. One of
                 the most relevant features of this new method is the
                 prediction of adaptive regulation thresholds for the
                 discretization of gene expression values, which is
                 required prior to the rule association learning
                 process. Moreover, an important advantage consists of
                 its low computational cost to infer association rules.
                 The proposed system may significantly support
                 exploratory, large-scale studies of automated
                 identification of potentially-relevant gene expression
                 associations.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "combinatorial optimization; decision trees; gene
                 expression data; genetic regulatory networks;
                 machine-learning",
}

@Article{Noman:2007:IGR,
  author =       "Nasimul Noman and Hitoshi Iba",
  title =        "Inferring Gene Regulatory Networks using Differential
                 Evolution with Local Search Heuristics",
  journal =      j-TCBB,
  volume =       "4",
  number =       "4",
  pages =        "634--647",
  month =        oct,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:58:47 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "We present a memetic algorithm for evolving the
                 structure of biomolecular interactions and inferring
                 the effective kinetic parameters from the time series
                 data of gene expression using the decoupled system
                 formalism. We propose an Information Criteria based
                 fitness evaluation for gene network model selection
                 instead of the conventional Mean Squared Error (MSE)
                 based fitness evaluation. A hill-climbing local-search
                 method has been incorporated in our evolutionary
                 algorithm for efficiently attaining the skeletal
                 architecture which is most frequently observed in
                 biological networks. The suitability of the method is
                 tested in gene circuit reconstruction experiments,
                 varying the network dimension and/or characteristics,
                 the amount of gene expression data used for inference
                 and the noise level present in expression profiles. The
                 reconstruction method inferred the network topology and
                 the regulatory parameters with high accuracy.
                 Nevertheless, the performance is limited to the amount
                 of expression data used and the noise level present in
                 the data. The proposed fitness function has been found
                 more suitable for identifying correct network topology
                 and for estimating the accurate parameter values
                 compared to the existing ones. Finally, we applied the
                 methodology for analyzing the cell-cycle gene
                 expression data of budding yeast and reconstructed the
                 network of some key regulators.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "biology and genetics; gene regulatory system; global
                 optimization; inverse problems; medicine and science;
                 memetic algorithm; microarray data; transcriptional
                 regulation",
}

@Article{Ho:2007:ITS,
  author =       "Shinn-Ying Ho and Chih-Hung Hsieh and Fu-Chieh Yu and
                 Hui-Ling Huang",
  title =        "An Intelligent Two-Stage Evolutionary Algorithm for
                 Dynamic Pathway Identification From Gene Expression
                 Profiles",
  journal =      j-TCBB,
  volume =       "4",
  number =       "4",
  pages =        "648--704",
  month =        oct,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:58:47 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "From gene expression profiles, it is desirable to
                 rebuild cellular dynamic regulation networks to
                 discover more delicate and substantial functions in
                 molecular biology, biochemistry, bioengineering and
                 pharmaceutics. S-system model is suitable to
                 characterize biochemical network systems and capable to
                 analyze the regulatory system dynamics. However,
                 inference of an S-system model of N-gene genetic
                 networks has 2N(N+1) parameters in a set of non-linear
                 differential equations to be optimized. This paper
                 proposes an intelligent two-stage evolutionary
                 algorithm (iTEA) to efficiently infer the S-system
                 models of genetic networks from time-series data of
                 gene expression. To cope with curse of dimensionality,
                 the proposed algorithm consists of two stages where
                 each uses a divide-and-conquer strategy. The
                 optimization problem is first decomposed into $N$
                 subproblems having 2(N+1) parameters each. At the first
                 stage, each subproblem is solved using a novel
                 intelligent genetic algorithm (IGA) with intelligent
                 crossover based on orthogonal experimental design
                 (OED). At the second stage, the obtained $N$ solutions
                 to the $N$ subproblems are combined and refined using
                 an OED-based simulated annealing algorithm for handling
                 noisy gene expression profiles. The effectiveness of
                 iTEA is evaluated using simulated expression patterns
                 with and without noise running on a single-processor
                 PC. It is shown that (1) IGA is efficient enough to
                 solve subproblems; (2) IGA is significantly superior to
                 the existing method SPXGA; and (3) iTEA performs well
                 in inferring S-system models for dynamic pathway
                 identification.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "divide-and-conquer; evolutionary algorithm; genetic
                 network; orthogonal experimental design; pathway
                 identification; S-system model",
}

@Article{Bereg:2007:PNB,
  author =       "Sergey Bereg and Yuanyi Zhang",
  title =        "Phylogenetic Networks Based on the Molecular Clock
                 Hypothesis",
  journal =      j-TCBB,
  volume =       "4",
  number =       "4",
  pages =        "661--667",
  month =        oct,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:58:47 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "A classical result in phylogenetic trees is that a
                 binary phylogenetic tree adhering to the molecular
                 clock hypothesis exists if and only if the matrix of
                 distances between taxa is ultrametric. The ultrametric
                 condition is very restrictive. In this paper we study
                 phylogenetic networks that can be constructed assuming
                 the molecular clock hypothesis. We characterize
                 distance matrices that admit such networks for 3 and 4
                 taxa. We also design two algorithms for constructing
                 networks optimizing the least-squares fit.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "least-squares fit; molecular clock hypothesis;
                 Phylogenetic Networks",
}

@Article{Blazewicz:2007:SPD,
  author =       "Jacek Blazewicz and Edmund Burke and Marta Kasprzak
                 and Alexandr Kovalev and Mikhail Kovalyov",
  title =        "Simplified Partial Digest Problem: Enumerative and
                 Dynamic Programming Algorithms",
  journal =      j-TCBB,
  volume =       "4",
  number =       "4",
  pages =        "668--680",
  month =        oct,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:58:47 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "We study the Simplified Partial Digest Problem (SPDP),
                 which is a mathematical model for a new simplified
                 partial digest method of genome mapping. This method is
                 easy for laboratory implementation and robust with
                 respect to the experimental errors. SPDP is NP-hard in
                 the strong sense. We present an $ O(n2^n) $ time
                 enumerative algorithm and an $ O(n^{2q}) $ time dynamic
                 programming algorithm for the error-free SPDP, where
                 $n$ is the number of restriction sites and $q$ is the
                 number of distinct intersite distances. We also give
                 examples of the problem, in which there are $ 2^{\frac
                 {n + 23} - 1} $ non-congruent solutions. These examples
                 partially answer a question recently posed in the
                 literature about the number of solutions of SPDP. We
                 adapt our enumerative algorithm for handling SPDP with
                 imprecise input data. Finally, we describe and discuss
                 the results of the computer experiments with our
                 algorithms.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "algorithm design and analysis; dynamic programming;
                 genome mapping; imprecise information; restriction site
                 analysis",
}

@Article{Xu:2007:IGR,
  author =       "Rui Xu and Donald {Wunsch II} and Ronald Frank",
  title =        "Inference of Genetic Regulatory Networks with
                 Recurrent Neural Network Models Using Particle Swarm
                 Optimization",
  journal =      j-TCBB,
  volume =       "4",
  number =       "4",
  pages =        "681--692",
  month =        oct,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:58:47 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Genetic regulatory network inference is critically
                 important for revealing fundamental cellular processes,
                 investigating gene functions, and understanding their
                 relations. The availability of time series gene
                 expression data makes it possible to investigate the
                 gene activities of whole genomes, rather than those of
                 only a pair of genes or among several genes. However,
                 current computational methods do not sufficiently
                 consider the temporal behavior of this type of data and
                 lack the capability to capture the complex nonlinear
                 system dynamics. We propose a recurrent neural network
                 (RNN) and particle swarm optimization (PSO) approach to
                 infer genetic regulatory networks from time series gene
                 expression data. Under this framework, gene interaction
                 is explained through a connection weight matrix. Based
                 on the fact that the measured time points are limited
                 and the assumption that the genetic networks are
                 usually sparsely connected, we present a PSO-based
                 search algorithm to unveil potential genetic network
                 constructions that fit well with the time series data
                 and explore possible gene interactions. Furthermore,
                 PSO is used to train the RNN and determine the network
                 parameters. Our approach has been applied to both
                 synthetic and real data sets. The results demonstrate
                 that the RNN\slash PSO can provide meaningful insights
                 in understanding the nonlinear dynamics of the gene
                 expression time series and revealing potential
                 regulatory interactions between genes.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "genetic regulatory networks; particle swarm
                 optimization; recurrent neural networks; time series
                 gene expression data",
}

@Article{Agius:2007:TSA,
  author =       "Phaedra Agius and Barry Kreiswirth and Steve Naidich
                 and Kristin Bennett",
  title =        "Typing Staphylococcus aureus Using the spa Gene and
                 Novel Distance Measures",
  journal =      j-TCBB,
  volume =       "4",
  number =       "4",
  pages =        "693--704",
  month =        oct,
  year =         "2007",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:58:47 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "We developed an approach for identifying groups or
                 families of Staphylococcus aureus bacteria based on
                 genotype data. With the emergence of drug resistant
                 strains, {\em S. aureus} represents a significant human
                 health threat. Identifying the family types efficiently
                 and quickly is crucial in community settings. Here, we
                 develop a hybrid sequence algorithm approach to type
                 this bacterium using only its spa gene. Two of the
                 sequence algorithms we used are well established, while
                 the third, the Best Common Gap-Weighted Sequence
                 (BCGS), is novel. We combined the sequence algorithms
                 with a weighted match/mismatch algorithm for the spa
                 sequence ends. Normalized similarity scores and
                 distances between the sequences were derived and used
                 within unsupervised clustering methods. The resulting
                 spa groupings correlated strongly with the groups
                 defined by the well-established Multi locus sequence
                 typing (MLST) method. Spa typing is preferable to MLST
                 typing which types seven genes instead of just one.
                 Furthermore, our spa clustering methods can be
                 fine-tuned to be more discriminative than MLST,
                 identifying new strains that the MLST method may not.
                 Finally, we performed a multidimensional scaling of our
                 distance matrices to visualize the relationship between
                 isolates. The proposed methodology provides a promising
                 new approach to molecular epidemiology.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "clustering; genotyping; molecular epidemiology;
                 sequence algorithms; staphylococcus aureus",
}

@Article{Congdon:2008:EIC,
  author =       "Clare Bates Congdon and Joseph C. Aman and Gerardo M.
                 Nava and H. Rex Gaskins and Carolyn J. Mattingly",
  title =        "An Evaluation of Information Content as a Metric for
                 the Inference of Putative Conserved Noncoding Regions
                 in {DNA} Sequences Using a Genetic Algorithms
                 Approach",
  journal =      j-TCBB,
  volume =       "5",
  number =       "1",
  pages =        "1--14",
  month =        jan,
  year =         "2008",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:59:11 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "In previous work, we presented GAMI [1], an approach
                 to motif inference that uses a genetic algorithms
                 search. GAMI is designed specifically to find putative
                 conserved regulatory motifs in noncoding regions of
                 divergent species, and is designed to allow for
                 analysis of long nucleotide sequences. In this work, we
                 compare GAMI's performance when run with its original
                 fitness function (a simple count of the number of
                 matches) and when run with information content, as well
                 as several variations on these metrics. Results
                 indicate that information content does not identify
                 highly conserved regions, and thus is not the
                 appropriate metric for this task, while variations on
                 information content as well as the original metric
                 succeed in identifying putative conserved regions.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "biology and genetics; evolutionary computing and
                 genetic algorithms",
}

@Article{Boscolo:2008:ITE,
  author =       "Riccardo Boscolo and James C. Liao and Vwani P.
                 Roychowdhury",
  title =        "An Information Theoretic Exploratory Method for
                 Learning Patterns of Conditional Gene Coexpression from
                 Microarray Data",
  journal =      j-TCBB,
  volume =       "5",
  number =       "1",
  pages =        "15--24",
  month =        jan,
  year =         "2008",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:59:11 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "In this article, we introduce an exploratory framework
                 for learning patterns of conditional co-expression in
                 gene expression data. The main idea behind the proposed
                 approach consists of estimating how the information
                 content shared by a set of $M$ nodes in a network
                 (where each node is associated to an expression
                 profile) varies upon conditioning on a set of L
                 conditioning variables (in the simplest case
                 represented by a separate set of expression profiles).
                 The method is non-parametric and it is based on the
                 concept of statistical co-information, which, unlike
                 conventional correlation based techniques, is not
                 restricted in scope to linear conditional dependency
                 patterns. Moreover, such conditional co-expression
                 relationships can potentially indicate regulatory
                 interactions that do not manifest themselves when only
                 pair-wise relationships are considered. A moment based
                 approximation of the co-information measure is derived
                 that efficiently gets around the problem of estimating
                 high-dimensional multi-variate probability density
                 functions from the data, a task usually not viable due
                 to the intrinsic sample size limitations that
                 characterize expression level measurements. By applying
                 the proposed exploratory method, we analyzed a whole
                 genome microarray assay of the eukaryote Saccharomices
                 cerevisiae and were able to learn statistically
                 significant patterns of conditional co-expression. A
                 selection of such interactions that carry a meaningful
                 biological interpretation are discussed.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "co-information; entropy; gene expression data;
                 information theory; statistical analysis",
}

@Article{Wiese:2008:REA,
  author =       "Kay C. Wiese and Alain A. Deschenes and Andrew G.
                 Hendriks",
  title =        "{RnaPredict---An} Evolutionary Algorithm for {RNA}
                 Secondary Structure Prediction",
  journal =      j-TCBB,
  volume =       "5",
  number =       "1",
  pages =        "25--41",
  month =        jan,
  year =         "2008",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:59:11 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "This paper presents two in-depth studies on
                 RnaPredict, an evolutionary algorithm for RNA secondary
                 structure prediction. The first study is an analysis of
                 the performance of two thermodynamic models, INN and
                 INN-HB. The correlation between the free energy of
                 predicted structures and the sensitivity is analyzed
                 for 19 RNA sequences. Although some variance is shown,
                 there is a clear trend between a lower free energy and
                 an increase in true positive base pairs. With
                 increasing sequence length, this correlation generally
                 decreases. In the second experiment, the accuracy of
                 the predicted structures for these 19 sequences are
                 compared against the accuracy of the structures
                 generated by the mfold dynamic programming algorithm
                 (DPA) and also to known structures. RnaPredict is shown
                 to outperform the minimum free energy structures
                 produced by mfold and has comparable performance when
                 compared to sub-optimal structures produced by mfold.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "evolutionary computation; RNA secondary structure
                 prediction; RnaPredict",
}

@Article{Rother:2008:SCP,
  author =       "Diego Rother and Guillermo Sapiro and Vijay Pande",
  title =        "Statistical Characterization of Protein Ensembles",
  journal =      j-TCBB,
  volume =       "5",
  number =       "1",
  pages =        "42--55",
  month =        jan,
  year =         "2008",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:59:11 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "When accounting for structural fluctuations or
                 measurement errors, a single rigid structure may not be
                 sufficient to represent a protein. One approach to
                 solve this problem is to represent the possible
                 conformations as a discrete set of observed
                 conformations, an ensemble. In this work, we follow a
                 different richer approach, and introduce a framework
                 for estimating probability density functions in very
                 high dimensions, and then apply it to represent
                 ensembles of folded proteins. This proposed approach
                 combines techniques such as kernel density estimation,
                 maximum likelihood, cross-validation, and
                 bootstrapping. We present the underlying theoretical
                 and computational framework and apply it to artificial
                 data and protein ensembles obtained from molecular
                 dynamics simulations. We compare the results with those
                 obtained experimentally, illustrating the potential and
                 advantages of this representation.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "Bayesian networks; bootstrapping; cross-validation;
                 density estimation; graphical models; maximum
                 likelihood; protein ensembles",
}

@Article{Cui:2008:AAU,
  author =       "Yun Cui and Lusheng Wang and Daming Zhu and Xiaowen
                 Liu",
  title =        "A $ (1.5 + {\epsilon }) $-Approximation Algorithm for
                 Unsigned Translocation Distance",
  journal =      j-TCBB,
  volume =       "5",
  number =       "1",
  pages =        "56--66",
  month =        jan,
  year =         "2008",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:59:11 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Genome rearrangement is an important area in
                 computational biology and bioinformatics. The
                 translocation operation is one of the popular
                 operations for genome rearrangement. It was proved that
                 computing the unsigned translocation distance is
                 NP-hard. In this paper, we present a $ (1.5 + \epsilon)
                 $-approximation algorithm for computing unsigned
                 translocation distance which improves upon the best
                 known 1.75-ratio. The running time of our algorithm is
                 $ O(n^2 + (4 / \epsilon)^1.5 \surd \log (4 / \epsilon)2
                 4^\epsilon) $, where $n$ is the total number of genes
                 in the genome.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "and approximation algorithms; genome rearrangement;
                 unsigned translocation",
}

@Article{Tan:2008:NBP,
  author =       "Tuan Zea Tan and Geok See Ng and Chai Quek",
  title =        "A Novel Biologically and Psychologically Inspired
                 Fuzzy Decision Support System: Hierarchical
                 Complementary Learning",
  journal =      j-TCBB,
  volume =       "5",
  number =       "1",
  pages =        "67--79",
  month =        jan,
  year =         "2008",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:59:11 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "A computational intelligent system that models the
                 human cognitive abilities may promise significant
                 performance in problem learning because human is
                 effective in learning and problem solving. Functionally
                 modeling the human cognitive abilities not only avoids
                 the details of the underlying neural mechanisms
                 performing the tasks, but also reduces the complexity
                 of the system. The complementary learning mechanism is
                 responsible for human pattern recognition, i.e. human
                 attends to positive and negative samples when making
                 decision. Furthermore, human concept learning is
                 organized in a hierarchical fashion. Such hierarchical
                 organization allows the divide-and-conquer approach to
                 the problem. Thus, integrating the functional models of
                 hierarchical organization and complementary learning
                 can potentially improve the performance in pattern
                 recognition. Hierarchical complementary learning
                 exhibits many of the desirable features of pattern
                 recognition. It is further supported by the
                 experimental results that verify the rationale of the
                 integration and that the hierarchical complementary
                 learning system is a promising pattern recognition
                 tool.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "cognitive learning; complementary learning; decision
                 support; fuzzy neural network; hierarchical model",
}

@Article{Ciocchetta:2008:ATS,
  author =       "Federica Ciocchetta and Corrado Priami and Paola
                 Quaglia",
  title =        "An Automatic Translation of {SBML} into Beta-Binders",
  journal =      j-TCBB,
  volume =       "5",
  number =       "1",
  pages =        "80--90",
  month =        jan,
  year =         "2008",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:59:11 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "A translation of SBML (Systems Biology Markup
                 Language) into a process algebra is proposed in order
                 to allow the formal specification, the simulation and
                 the formal analysis of biological models. Beta-binders,
                 a language with a quantitative stochastic extension, is
                 chosen for the translation. The proposed translation
                 focuses on the main components of SBML models, as
                 species and reactions. Furthermore, it satisfies the
                 compositional property, i.e. the translation of the
                 whole model is obtained by composing the translation of
                 the subcomponents. An automatic translator tool of SBML
                 models into Beta-binders has been implemented as well.
                 Finally, the translation of a simple model is
                 reported.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "biological systems; modeling; Process algebras;
                 systems biology; Systems Biology Markup Language
                 (SBML); translation tool",
}

@Article{Bocker:2008:CAM,
  author =       "Sebastian Bocker and Veli Makinen",
  title =        "Combinatorial Approaches for Mass Spectra
                 Recalibration",
  journal =      j-TCBB,
  volume =       "5",
  number =       "1",
  pages =        "91--100",
  month =        jan,
  year =         "2008",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:59:11 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Mass spectrometry has become one of the most popular
                 analysis techniques in Proteomics and Systems Biology.
                 With the creation of larger datasets, the automated
                 recalibration of mass spectra becomes important to
                 ensure that every peak in the sample spectrum is
                 correctly assigned to some peptide and protein.
                 Algorithms for recalibrating mass spectra have to be
                 robust with respect to wrongly assigned peaks, as well
                 as efficient due to the amount of mass spectrometry
                 data. The recalibration of mass spectra leads us to the
                 problem of finding an optimal matching between mass
                 spectra under measurement errors. We have developed two
                 deterministic methods that allow robust computation of
                 such a matching: The first approach uses a
                 computational geometry interpretation of the problem,
                 and tries to find two parallel lines with constant
                 distance that stab a maximal number of points in the
                 plane. The second approach is based on finding a
                 maximal common approximate subsequence, and improves
                 existing algorithms by one order of magnitude
                 exploiting the sequential nature of the matching
                 problem. We compare our results to a computational
                 geometry algorithm using a topological line-sweep.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "biotechnology; combinatorial pattern matching;
                 computational geometry; mass spectrometry",
}

@Article{Barzuza:2008:CPP,
  author =       "Tamar Barzuza and Jacques S. Beckmann and Ron Shamir
                 and Itsik Pe'er",
  title =        "Computational Problems in Perfect Phylogeny
                 Haplotyping: Typing without Calling the Allele",
  journal =      j-TCBB,
  volume =       "5",
  number =       "1",
  pages =        "101--109",
  month =        jan,
  year =         "2008",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:59:11 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "A haplotype is an m-long binary vector. The
                 xor-genotype of two haplotypes is the m-vector of their
                 coordinate-wise xor. We study the following problem:
                 Given a set of xor-genotypes, reconstruct their
                 haplotypes so that the set of resulting haplotypes can
                 be mapped onto a perfect phylogeny tree. The question
                 is motivated by studying population evolution in human
                 genetics, and is a variant of the perfect phylogeny
                 haplotyping problem that has received intensive
                 attention recently. Unlike the latter problem, in which
                 the input is `full' genotypes, here we assume less
                 informative input, and so may be more economical to
                 obtain experimentally. Building on ideas of Gusfield,
                 we show how to solve the problem in polynomial time, by
                 a reduction to the graph realization problem. The
                 actual haplotypes are not uniquely determined by that
                 tree they map onto, and the tree itself may or may not
                 be unique. We show that tree uniqueness implies
                 uniquely determined haplotypes, up to inherent degrees
                 of freedom, and give a sufficient condition for the
                 uniqueness. To actually determine the haplotypes given
                 the tree, additional information is necessary. We show
                 that two or three full genotypes suffice to reconstruct
                 all the haplotypes, and present a linear algorithm for
                 identifying those genotypes.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "graph realization; haplotypes; perfect phylogeny;
                 XOR-genotypes",
}

@Article{Chin:2008:DMR,
  author =       "Francis Chin and Henry C. M. Leung",
  title =        "{DNA} Motif Representation with Nucleotide
                 Dependency",
  journal =      j-TCBB,
  volume =       "5",
  number =       "1",
  pages =        "110--119",
  month =        jan,
  year =         "2008",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:59:11 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The problem of discovering novel motifs of binding
                 sites is important to the understanding of gene
                 regulatory networks. Motifs are generally represented
                 by matrices (PWM or PSSM) or strings. However, these
                 representations cannot model biological binding sites
                 well because they fail to capture nucleotide
                 interdependence. It has been pointed out by many
                 researchers that the nucleotides of the DNA binding
                 site cannot be treated independently, e.g. the binding
                 sites of zinc finger in proteins. In this paper, a new
                 representation called Scored PositionSpecific Pattern
                 (SPSP), which is a generalization of the matrix and
                 string representations, is introduced which takes into
                 consideration the dependent occurrences of neighboring
                 nucleotides. Even though the problem of discovering the
                 optimal motif in SPSP representation is proved to
                 beNP-hard, we introduce a heuristic algorithm called
                 SPSP-Finder, which can effectively find optimal motifs
                 in most simulated cases and some real cases for which
                 existing popular motif finding software, such as
                 Weeder, MEME and AlignACE, fail.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "Computing methodologies; design methodology; pattern
                 analysis; pattern recognition",
}

@Article{Yin:2008:NAC,
  author =       "Zong-Xian Yin and Jung-Hsien Chiang",
  title =        "Novel Algorithm for Coexpression Detection in
                 Time-Varying Microarray Data Sets",
  journal =      j-TCBB,
  volume =       "5",
  number =       "1",
  pages =        "120--135",
  month =        jan,
  year =         "2008",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:59:11 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "When analyzing the results of microarray experiments,
                 biologists generally use unsupervised categorization
                 tools. However, such tools regard each time point as an
                 independent dimension and utilize the Euclidean
                 distance to compute the similarities between
                 expressions. Furthermore, some of these methods require
                 the number of clusters to be determined in advance,
                 which is clearly impossible in the case of a new
                 dataset. Therefore, this study proposes a novel scheme,
                 designated as the Variation-based Co-expression
                 Detection (VCD) algorithm, to analyze the trends of
                 expressions based on their variation over time. The
                 proposed algorithm has two advantages. First, it is
                 unnecessary to determine the number of clusters in
                 advance since the algorithm automatically detects those
                 genes whose profiles are grouped together and creates
                 patterns for these groups. Second, the algorithm
                 features a new measurement criterion for calculating
                 the degree of change of the expressions between
                 adjacent time points and evaluating their trend
                 similarities. Three real-world microarray datasets are
                 employed to evaluate the performance of the proposed
                 algorithm.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "bioinformatics; clustering; data mining; gene
                 expression; pattern analysis; time series analysis",
}

@Article{Goeffon:2008:PTN,
  author =       "Adrien Goeffon and Jean-Michel Richer and Jin-Kao
                 Hao",
  title =        "Progressive Tree Neighborhood Applied to the Maximum
                 Parsimony Problem",
  journal =      j-TCBB,
  volume =       "5",
  number =       "1",
  pages =        "136--145",
  month =        jan,
  year =         "2008",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:59:11 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The Maximum Parsimony problem aims at reconstructing a
                 phylogenetic tree from DNA sequences while minimizing
                 the number of genetic transformations. To solve this
                 NP-complete problem, heuristic methods have been
                 developed, often based on local search. In this
                 article, we focus on the influence of the neighborhood
                 relations. After analyzing the advantages and drawbacks
                 of the well-known NNI, SPR and TBR neighborhoods, we
                 introduce the concept of Progressive Neighborhood which
                 consists in constraining progressively the size of the
                 neighborhood as the search advances. We empirically
                 show that applied to the Maximum Parsimony problem,
                 this progressive neighborhood turns out to be more
                 efficient and robust than the classic neighborhoods
                 using a descent algorithm. Indeed, it allows to find
                 better solutions with a smaller number of iterations or
                 trees evaluated.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "combinatorial algorithms; maximum parsimony;
                 optimization; phylogeny reconstruction",
}

@Article{Anonymous:2008:RL,
  author =       "Anonymous",
  title =        "2007 Reviewers List",
  journal =      j-TCBB,
  volume =       "5",
  number =       "1",
  pages =        "146--147",
  month =        jan,
  year =         "2008",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:59:11 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Anonymous:2008:AI,
  author =       "Anonymous",
  title =        "2007 Annual Index",
  journal =      j-TCBB,
  volume =       "5",
  number =       "1",
  pages =        "148--158",
  month =        jan,
  year =         "2008",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:59:11 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Anonymous:2008:CAE,
  author =       "Anonymous",
  title =        "Call for Applications for {Editor-in-Chief}",
  journal =      j-TCBB,
  volume =       "5",
  number =       "1",
  pages =        "159--159",
  month =        jan,
  year =         "2008",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:59:11 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Jackson:2008:CGM,
  author =       "Benjamin N. Jackson and Patrick S. Schnable and
                 Srinivas Aluru",
  title =        "Consensus Genetic Maps as Median Orders from
                 Inconsistent Sources",
  journal =      j-TCBB,
  volume =       "5",
  number =       "2",
  pages =        "161--171",
  month =        apr,
  year =         "2008",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:59:29 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "A genetic map is an ordering of genetic markers
                 calculated from a population of known lineage. While
                 traditionally a map has been generated from a single
                 population for each species, recently researchers have
                 created maps from multiple populations. In the face of
                 these new data, we address the need to find a consensus
                 map --- a map that combines the information from
                 multiple partial and possibly inconsistent input maps.
                 We model each input map as a partial order and
                 formulate the consensus problem as finding a median
                 partial order. Finding the median of multiple total
                 orders (preferences or rankings)is a well studied
                 problem in social choice. We choose to find the median
                 using the weighted symmetric difference distance, a
                 more general version of both the symmetric difference
                 distance and the Kemeny distance. Finding a median
                 order using this distance is NP-hard. We show that for
                 our chosen weight assignment, a median order satisfies
                 the positive responsiveness, extended Condorcet,and
                 unanimity criteria. Our solution involves finding the
                 maximum acyclic subgraph of a weighted directed graph.
                 We present a method that dynamically switches between
                 an exact branch and bound algorithm and a heuristic
                 algorithm, and show that for real data from closely
                 related organisms, an exact median can often be found.
                 We present experimental results using seven populations
                 of the crop plant {\em Zea mays}.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "genetic map; Kemeny distance; median order; path and
                 circuit problems; symmetric difference distance.",
}

@Article{Gupta:2008:EDS,
  author =       "Anupam Gupta and Ziv Bar-Joseph",
  title =        "Extracting Dynamics from Static Cancer Expression
                 Data",
  journal =      j-TCBB,
  volume =       "5",
  number =       "2",
  pages =        "172--182",
  month =        apr,
  year =         "2008",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:59:29 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Static expression experiments analyze samples from
                 many individuals. These samples are often snapshots of
                 the progression of a certain disease such as cancer.
                 This raises an intriguing question: Can we determine a
                 temporal order for these samples? Such an ordering can
                 lead to better understanding of the dynamics of the
                 disease and to the identification of genes associated
                 with its progression. In this paper we formally prove,
                 for the first time, that under a model for the dynamics
                 of the expression levels of a single gene, it is indeed
                 possible to recover the correct ordering of the static
                 expression datasets by solving an instance of the
                 traveling salesman problem (TSP). In addition, we
                 devise an algorithm that combines a TSP heuristic and
                 probabilistic modeling for inferring the underlying
                 temporal order of the microarray experiments. This
                 algorithm constructs probabilistic continuous curves to
                 represent expression profiles leading to accurate
                 temporal reconstruction for human data. Applying our
                 method to cancer expression data we show that the
                 ordering derived agrees well with survival duration. A
                 classifier that utilizes this ordering improves upon
                 other classifiers suggested for this task. The set of
                 genes displaying consistent behavior for the determined
                 ordering are enriched for genes associated with cancer
                 progression.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "EM; glioma; microarrays; traveling salesman",
}

@Article{Thomas:2008:GMR,
  author =       "John Thomas and Naren Ramakrishnan and Chris
                 Bailey-Kellogg",
  title =        "Graphical Models of Residue Coupling in Protein
                 Families",
  journal =      j-TCBB,
  volume =       "5",
  number =       "2",
  pages =        "183--197",
  month =        apr,
  year =         "2008",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:59:29 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Many statistical measures and algorithmic techniques
                 have been proposed for studying residue coupling in
                 protein families. Generally speaking, two residue
                 positions are considered coupled if, in the sequence
                 record, some of their amino acid type combinations are
                 significantly more common than others. While the
                 proposed approaches have proven useful in finding and
                 describing coupling, a significant missing component is
                 a formal probabilistic model that explicates and
                 compactly represents the coupling, integrates
                 information about sequence,structure, and function, and
                 supports inferential procedures for analysis,
                 diagnosis, and prediction. We present an approach to
                 learning and using probabilistic graphical models of
                 residue coupling. These models capture significant
                 conservation and coupling constraints observable in a
                 multiply-aligned set of sequences. Our approach can
                 place a structural prior on considered couplings, so
                 that all identified relationships have direct
                 mechanistic explanations. It can also incorporate
                 information about functional classes, and thereby learn
                 a differential graphical model that distinguishes
                 constraints common to all classes from those unique to
                 individual classes. Such differential models separately
                 account for class-specific conservation and family-wide
                 coupling, two different sources of sequence
                 covariation. They are then able to perform
                 interpretable functional classification of new
                 sequences, explaining classification decisions in terms
                 of the underlying conservation and coupling
                 constraints. We apply our approach in studies of both G
                 protein-coupled receptors and PDZ domains, identifying
                 and analyzing family-wide and class-specific
                 constraints, and performing functional classification.
                 The results demonstrate that graphical models of
                 residue coupling provide a powerful tool for
                 uncovering, representing, and utilizing significant
                 sequence structure-function relationships in protein
                 families.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "correlated mutations; evolutionary covariation;
                 functional classification; graphical models;
                 sequence-structure-function relationships",
}

@Article{Mena-Chalco:2008:IPC,
  author =       "Jesus Mena-Chalco and Helaine Carrer and Yossi Zana
                 and Roberto M. {Cesar Jr.}",
  title =        "Identification of Protein Coding Regions Using the
                 Modified {Gabor}-Wavelet Transform",
  journal =      j-TCBB,
  volume =       "5",
  number =       "2",
  pages =        "198--207",
  month =        apr,
  year =         "2008",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:59:29 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "An important topic in genomic sequence analysis is the
                 identification of protein coding regions. In this
                 context, several coding DNA model-independent methods,
                 based on the occurrence of specific patterns of
                 nucleotides at coding regions, have been proposed.
                 Nonetheless, these methods have not been completely
                 suitable due to their dependence on an empirically
                 pre-defined window length required for a local analysis
                 of a DNA region. We introduce a method, based on a
                 modified Gabor-wavelet transform (MGWT), for the
                 identification of protein coding regions. This novel
                 transform is tuned to analyze periodic signal
                 components and presents the advantage of being
                 independent of the window length. We compared the
                 performance of the MGWT with other methods using
                 eukaryote datasets. The results show that the MGWT
                 outperforms all assessed model-independent methods with
                 respect to identification accuracy. These results
                 indicate that the source of at least part of the
                 identification errors produced by the previous methods
                 is the fixed working scale. The new method not only
                 avoids this source of errors, but also makes available
                 a tool for detailed exploration of the nucleotide
                 occurrence.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "biology and genetics; pattern recognition; signal
                 processing",
}

@Article{deJong:2008:SSS,
  author =       "Hidde de Jong and Michel Page",
  title =        "Search for Steady States of Piecewise-Linear
                 Differential Equation Models of Genetic Regulatory
                 Networks",
  journal =      j-TCBB,
  volume =       "5",
  number =       "2",
  pages =        "208--222",
  month =        apr,
  year =         "2008",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:59:29 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Analysis of the attractors of a genetic regulatory
                 network gives a good indication of the possible
                 functional modes of the system. In this paper we are
                 concerned with the problem of finding all steady states
                 of genetic regulatory networks described by
                 piecewise-linear differential equation (PLDE) models.
                 We show that the problem is NP-hard and translate it
                 into a propositional satisfiability (SAT) problem. This
                 allows the use of existing, efficient SAT solvers and
                 has enabled the development of a steady state search
                 module of the computer tool Genetic Network Analyzer
                 (GNA). The practical use of this module is demonstrated
                 by means of the analysis of a number of relatively
                 small bacterial regulatory networks as well as randomly
                 generated networks of several hundreds of genes.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "genetic regulatory networks; large-scale systems;
                 piecewise-linear differential equations; SAT problem;
                 steady states",
}

@Article{Sadot:2008:TVB,
  author =       "Avital Sadot and Jasmin Fisher and Dan Barak and
                 Yishai Admanit and Michael J. Stern and E. Jane Albert
                 Hubbard and David Harel",
  title =        "Toward Verified Biological Models",
  journal =      j-TCBB,
  volume =       "5",
  number =       "2",
  pages =        "223--234",
  month =        apr,
  year =         "2008",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:59:29 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The last several decades have witnessed a vast
                 accumulation of biological data and data analysis. Many
                 of these data sets represent only a small fraction of
                 the system's behavior, making the visualization of full
                 system behavior difficult. A more complete
                 understanding of a biological system is gained when
                 different types of data (and/or conclusions drawn from
                 the data) are integrated into a larger-scale
                 representation or model of the system. Ideally, this
                 type of model is consistent with all available data
                 about the system, and it is then used to generate
                 additional hypotheses to be tested. Computer-based
                 methods intended to formulate models that integrate
                 various events and to test the consistency of these
                 models with respect to the laboratory-based
                 observations on which they are based are potentially
                 very useful. In addition, in contrast to informal
                 models, the consistency of such formal computer-based
                 models with laboratory data can be tested rigorously by
                 methods of formal verification. We combined two formal
                 modeling approaches in computer science that were
                 originally developed for non-biological system design.
                 One is the inter-object approach using the language of
                 live sequence charts (LSCs) with the Play-Engine tool,
                 and the other is the intra-object approach using the
                 language of statecharts and Rhapsody as the tool.
                 Integration is carried out using InterPlay, a
                 simulation engine coordinator. Using these tools, we
                 constructed a combined model comprising three modules.
                 One module represents the early lineage of the somatic
                 gonad of {\em C. elegans} in LSCs, while a second more
                 detailed module in statecharts represents an
                 interaction between two cells within this lineage that
                 determine their developmental outcome. Using the
                 advantages of the tools, we created a third module
                 representing a set of key experimental data using LSCs.
                 We tested the combined statechart-LSC model by showing
                 that the simulations were consistent with the set of
                 experimental LSCs. This small-scale modular example
                 demonstrates the potential for using similar approaches
                 for verification by exhaustive testing of models by
                 LSCs. It also shows the advantages of these approaches
                 for modeling biology.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "C. elegans; modeling; statecharts; verification",
}

@Article{Spillner:2008:CPD,
  author =       "Andreas Spillner and Binh T. Nguyen and Vincent
                 Moulton",
  title =        "Computing Phylogenetic Diversity for Split Systems",
  journal =      j-TCBB,
  volume =       "5",
  number =       "2",
  pages =        "235--244",
  month =        apr,
  year =         "2008",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:59:29 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "In conservation biology it is a central problem to
                 measure, predict, and preserve biodiversity as species
                 face extinction. In 1992 Faith proposed measuring the
                 diversity of a collection of species in terms of their
                 relationships on a phylogenetic tree, and to use this
                 information to identify collections of species with
                 high diversity. Here we are interested in some variants
                 of the resulting optimization problem that arise when
                 considering species whose evolution is better
                 represented by a network rather than a tree. More
                 specifically, we consider the problem of computing
                 phylogenetic diversity relative to a split system on a
                 collection of species of size $n$. We show that for
                 general split systems this problem is NP-hard. In
                 addition we provide some efficient algorithms for some
                 special classes of split systems, in particular
                 presenting an optimal $ O(n) $ time algorithm for
                 phylogenetic trees and an $ O(n \log n + n k) $ time
                 algorithm for choosing an optimal subset of size $k$
                 relative to a circular split system.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "biology and genetics; life and medical sciences",
}

@Article{Lancia:2008:HDA,
  author =       "Giuseppe Lancia and R. Ravi and Romeo Rizzi",
  title =        "Haplotyping for Disease Association: {A} Combinatorial
                 Approach",
  journal =      j-TCBB,
  volume =       "5",
  number =       "2",
  pages =        "245--251",
  month =        apr,
  year =         "2008",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:59:29 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "We consider a combinatorial problem derived from
                 haplotyping a population with respect to a genetic
                 disease, either recessive or dominant. Given a set of
                 individuals, partitioned into healthy and diseased, and
                 the corresponding sets of genotypes, we want to infer
                 ``bad'' and ``good'' haplotypes to account for these
                 genotypes and for the disease. Assume e.g. the disease
                 is recessive. Then, the resolving haplotypes must
                 consist of {\em bad\/} and {\em good\/} haplotypes, so
                 that (i) each genotype belonging to a diseased
                 individual is explained by a pair of bad haplotypes and
                 (ii) each genotype belonging to a healthy individual is
                 explained by a pair of haplotypes of which at least one
                 is good. We prove that the associated decision problem
                 is NP-complete. However, we also prove that there is a
                 simple solution, provided the data satisfy a very weak
                 requirement.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "biology and genetics; combinatorics; discrete
                 mathematics",
}

@Article{Gusev:2008:HSG,
  author =       "Alexander Gusev and Ion I. M{\~a}ndoiu and Bogdan
                 Pa{\c{s}}aniuc",
  title =        "Highly Scalable Genotype Phasing by Entropy
                 Minimization",
  journal =      j-TCBB,
  volume =       "5",
  number =       "2",
  pages =        "252--261",
  month =        apr,
  year =         "2008",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:59:29 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "A Single Nucleotide Polymorphism (SNP) is a position
                 in the genome at which two or more of the possible four
                 nucleotides occur in a large percentage of the
                 population. SNPsaccount for most of the genetic
                 variability between individuals,and mapping SNPs in the
                 human population has become the next high-priority in
                 genomics after the completion of the HumanGenome
                 project. In diploid organisms such as humans, there are
                 two non-identical copies of each autosomal chromosome.
                 A description of the SNPs in a chromosome is called a
                 haplotype. At present, it is prohibitively expensive to
                 directly determine the haplotypes of an individual, but
                 it is possible to obtain rather easily the conflated
                 SNP information in the so called genotype.
                 Computational methods for genotype phasing, i.e.,
                 inferring haplotypes from genotype data, have received
                 much attention in recent years as haplotype information
                 leads to increased statistical power of disease
                 association tests. However, many of the existing
                 algorithms have impractical running time for phasing
                 large genotype datasets such as those generated by the
                 international HapMap project. In this paper we propose
                 a highly scalable algorithm based on entropy
                 minimization. Our algorithm is capable of phasing both
                 unrelated and related genotypes coming from complex
                 pedigrees. Experimental results on both real and
                 simulated datasets show that our algorithm achieves a
                 phasing accuracy worse but close to that of best
                 existing methods while being several orders of
                 magnitude faster. The open source code implementation
                 of the algorithm and a web interface are publicly
                 available at
                 \path=http://dna.engr.uconn.edu/~software/ent/=.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "algorithm; genotype phasing; haplotype; Single
                 Nucleotide Polymorphism",
}

@Article{Zhao:2008:ICG,
  author =       "Wentao Zhao and Erchin Serpedin and Edward R.
                 Dougherty",
  title =        "Inferring Connectivity of Genetic Regulatory Networks
                 Using Information-Theoretic Criteria",
  journal =      j-TCBB,
  volume =       "5",
  number =       "2",
  pages =        "262--274",
  month =        apr,
  year =         "2008",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:59:29 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Recently, the concept of mutual information has been
                 proposed for infering the structure of genetic
                 regulatory networks from gene expression profiling.
                 After analyzing the limitations of mutual information
                 in inferring the gene-to-gene interactions, this paper
                 introduces the concept of conditional mutual
                 information and based on it proposes two novel
                 algorithms to infer the connectivity structure of
                 genetic regulatory networks. One of the proposed
                 algorithms exhibits a better accuracy while the other
                 algorithm excels in simplicity and flexibility. By
                 exploiting the mutual information and conditional
                 mutual information, a practical metric is also proposed
                 to assess the likeliness of direct connectivity between
                 genes. This novel metric resolves a common limitation
                 associated with the current inference algorithms,
                 namely the situations where the gene connectivity is
                 established in terms of the dichotomy of being either
                 connected or disconnected. Based on the data sets
                 generated by synthetic networks, the performance of the
                 proposed algorithms is compared favorably relative to
                 existing state-of-the-art schemes. The proposed
                 algorithms are also applied on realistic biological
                 measurements, such as the cutaneous melanoma data set,
                 and biological meaningful results are inferred.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "biology and genetics; DNA microarray; genetic
                 regulatory network; information theory",
}

@Article{Bordewich:2008:NRS,
  author =       "Magnus Bordewich and Charles Semple",
  title =        "Nature Reserve Selection Problem: {A} Tight
                 Approximation Algorithm",
  journal =      j-TCBB,
  volume =       "5",
  number =       "2",
  pages =        "275--280",
  month =        apr,
  year =         "2008",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:59:29 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The Nature Reserve Selection Problem is a problem that
                 arises in the context of studying biodiversity
                 conservation. Subject to budgetary constraints, the
                 problem is to select a set of regions to conserve so
                 that the phylogenetic diversity of the set of species
                 contained within those regions is maximized. Recently,
                 it was shown in a paper by Moulton {\em et al.} that
                 this problem is NP-hard. In this paper, we establish a
                 tight polynomial-time approximation algorithm for the
                 Nature Reserve Section Problem. Furthermore, we resolve
                 a question on the computational complexity of a related
                 problem left open in Moulton {\em et al.}",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "combinatorial algorithms; trees",
}

@Article{Hsieh:2008:OAI,
  author =       "Yong-Hsiang Hsieh and Chih-Chiang Yu and Biing-Feng
                 Wang",
  title =        "Optimal Algorithms for the Interval Location Problem
                 with Range Constraints on Length and Average",
  journal =      j-TCBB,
  volume =       "5",
  number =       "2",
  pages =        "281--290",
  month =        apr,
  year =         "2008",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:59:29 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Let $A$ be a sequence of $n$ real numbers, $ L_1 $ and
                 $ L_2 $ be two integers such that $ L_1 \leq L_2 $, and
                 $ R_1 $ and $ R_2 $ be two real numbers such that $ R_1
                 \leq R_2 $. An interval of $A$ is feasible if its
                 length is between $ L_1 $ and $ L_2 $ and its average
                 is between $ R_1 $ and $ R_2 $. In this paper, we study
                 the following problems: finding all feasible intervals
                 of $A$, counting all feasible intervals of $A$, finding
                 a maximum cardinality set of non-overlapping feasible
                 intervals of $A$, locating a longest feasible interval
                 of $A$, and locating a shortest feasible interval of
                 $A$. The problems are motivated from the problem of
                 locating CpG islands in biomolecular sequences. In this
                 paper, we firstly show that all the problems have an $
                 \Omega (n \log n) $-time lower bound in the comparison
                 model. Then, we use geometric approaches to design
                 optimal algorithms for the problems. All the presented
                 algorithms run in an on-line manner and use $ O(n) $
                 space.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "algorithms; analysis of algorithms; data structures;
                 geometrical problems and computations",
}

@Article{Lamers:2008:PRX,
  author =       "Susanna L. Lamers and Marco Salemi and Michael S.
                 McGrath and Gary B. Fogel",
  title =        "Prediction of {R5}, {X4}, and {R5X4} {HIV}-1
                 Coreceptor Usage with Evolved Neural Networks",
  journal =      j-TCBB,
  volume =       "5",
  number =       "2",
  pages =        "291--300",
  month =        apr,
  year =         "2008",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:59:29 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The HIV-1 genome is highly heterogeneous. This
                 variation affords the virus a wide range of molecular
                 properties, including the ability to infect cell types,
                 such as macrophages and lymphocytes, expressing
                 different chemokine receptors on the cell surface. In
                 particular, R5 HIV-1 viruses use CCR5 as co-receptor
                 for viral entry, X4 viruses use CXCR4, whereas some
                 viral strains, known as R5X4 or D-tropic, have the
                 ability to utilize both co-receptors. X4 and R5X4
                 viruses are associated with rapid disease progression
                 to AIDS. R5X4 viruses differ in that they have yet to
                 be characterized by the examination of the genetic
                 sequence of HIV-1 alone. In this study, a series of
                 experiments was performed to evaluate different
                 strategies of feature selection and neural network
                 optimization. We demonstrate the use of artificial
                 neural networks trained via evolutionary computation to
                 predict viral co-receptor usage. The results indicate
                 identification of R5X4 viruses with predictive accuracy
                 of 75.5\%.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "AIDS; artificial neural networks; Computational
                 intelligence; dual-tropic viruses; evolutionary
                 computation; HIV; phenotype prediction; tropism",
}

@Article{vanIersel:2008:SIT,
  author =       "Leo van Iersel and Judith Keijsper and Steven Kelk and
                 Leen Stougie",
  title =        "Shorelines of Islands of Tractability: Algorithms for
                 Parsimony and Minimum Perfect Phylogeny Haplotyping
                 Problems",
  journal =      j-TCBB,
  volume =       "5",
  number =       "2",
  pages =        "301--312",
  month =        apr,
  year =         "2008",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:59:29 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The problem Parsimony Haplotyping (PH) asks for the
                 smallest set of haplotypes which can explain a given
                 set of genotypes, and the problem Minimum Perfect
                 Phylogeny Haplotyping (MPPH) asks for the smallest such
                 set which also allows the haplotypes to be embedded in
                 a perfect phylogeny, an evolutionary tree with
                 biologically-motivated restrictions. For PH, we extend
                 recent work by further mapping the interface between
                 ``easy'' and ``hard'' instances, within the framework
                 of $ (k, l) $-bounded instances where the number of 2's
                 per column and row of the input matrix is restricted.
                 By exploring, in the same way, the tractability
                 frontier of MPPH we provide the first concrete,
                 positive results for this problem. In addition, we
                 construct for both PH and MPPH polynomial time
                 approximation algorithms, based on properties of the
                 columns of the input matrix.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "biology and genetics; combinatorial algorithms;
                 complexity hierarchies",
}

@Article{Brinza:2008:SPM,
  author =       "Dumitru Brinza and Alexander Zelikovsky",
  title =        "{2SNP}: Scalable Phasing Method for Trios and
                 Unrelated Individuals",
  journal =      j-TCBB,
  volume =       "5",
  number =       "2",
  pages =        "313--318",
  month =        apr,
  year =         "2008",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jun 12 16:59:29 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Emerging microarray technologies allow affordable
                 typing of very long genome sequences. A key challenge
                 in analyzing of such huge amount of data is scalable
                 and accurate computational inferring of haplotypes
                 (i.e., splitting of each genotype into a pair of
                 corresponding haplotypes). In this paper, we first
                 phase genotypes consisting only of two SNPs using
                 genotypes frequencies adjusted to the random mating
                 model and then extend phasing of two-SNP genotypes to
                 phasing of complete genotypes using maximum spanning
                 trees. Runtime of the proposed 2SNP algorithm is $ O(n
                 m (n + \log m)) $, where $n$ and $m$ are the numbers of
                 genotypes and SNPs, respectively, and it can handle
                 genotypes spanning entire chromosomes in a matter of
                 hours. On datasets across 23 chromosomal regions from
                 HapMap[11], 2SNP is several orders of magnitude faster
                 than GERBIL and PHASE while matching them in quality
                 measured by the number of correctly phased genotypes,
                 single-site and switching errors. For example the 2SNP
                 software phases entire chromosome ($ 10^5 $ SNPs from
                 HapMap) for 30 individuals in 2 hours with average
                 switching error 7.7\%. We have also enhanced 2SNP
                 algorithm to phase family trio data and compared it
                 with four other well-known phasing methods on simulated
                 data from [15]. 2SNP is much faster than all of them
                 while losing in quality only to PHASE. 2SNP software is
                 publicly available at
                 \path=http://alla.cs.gsu.edu/~software/2SNP=.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "algorithm; genotype; haplotype; phasing; SNP",
}

@Article{Mandoiu:2008:GEI,
  author =       "Ion I. Mandoiu and Yi Pan and Alexander Zelikovsky",
  title =        "{Guest Editors}' Introduction to the Special Section
                 on Bioinformatics Research and Applications",
  journal =      j-TCBB,
  volume =       "5",
  number =       "3",
  pages =        "321--322",
  month =        jul,
  year =         "2008",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.85",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Oct 10 12:59:44 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Sridhar:2008:MIL,
  author =       "Srinath Sridhar and Fumei Lam and Guy E. Blelloch and
                 R. Ravi and Russell Schwartz",
  title =        "Mixed Integer Linear Programming for Maximum-Parsimony
                 Phylogeny Inference",
  journal =      j-TCBB,
  volume =       "5",
  number =       "3",
  pages =        "323--331",
  month =        jul,
  year =         "2008",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.26",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Oct 10 12:59:44 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Reconstruction of phylogenetic trees is a fundamental
                 problem in computational biology. While excellent
                 heuristic methods are available for many variants of
                 this problem, new advances in phylogeny inference will
                 be required if we are to be able to continue to make
                 effective use of the rapidly growing stores of
                 variation data now being gathered. In this paper, we
                 present two integer linear programming (ILP)
                 formulations to find the most parsimonious phylogenetic
                 tree from a set of binary variation data. One method
                 uses a flow-based formulation that can produce
                 exponential numbers of variables and constraints in the
                 worst case. The method has, however, proven extremely
                 efficient in practice on datasets that are well beyond
                 the reach of the available provably efficient methods,
                 solving several large mtDNA and Y-chromosome instances
                 within a few seconds and giving provably optimal
                 results in times competitive with fast heuristics than
                 cannot guarantee optimality. An alternative formulation
                 establishes that the problem can be solved with a
                 polynomial-sized ILP. We further present a web server
                 developed based on the exponential-sized ILP that
                 performs fast maximum parsimony inferences and serves
                 as a front end to a database of precomputed phylogenies
                 spanning the human genome.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "algorithms; computational biology; integer linear
                 programming; maximum parsimony; phylogenetic tree
                 reconstruction; Steiner tree problem",
}

@Article{Bernt:2008:SPR,
  author =       "Matthias Bernt and Daniel Merkle and Martin
                 Middendorf",
  title =        "Solving the Preserving Reversal Median Problem",
  journal =      j-TCBB,
  volume =       "5",
  number =       "3",
  pages =        "332--347",
  month =        jul,
  year =         "2008",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.39",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Oct 10 12:59:44 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Genomic rearrangement operations can be very useful to
                 infer the phylogenetic relationship of gene orders
                 representing species. We study the problem of finding
                 potential ancestral gene orders for the gene orders of
                 given taxa, such that the corresponding rearrangement
                 scenario has a minimal number of reversals, and where
                 each of the reversals has to preserve the common
                 intervals of the given input gene orders. Common
                 intervals identify sets of genes that occur
                 consecutively in all input gene orders. The problem of
                 finding such an ancestral gene order is called the
                 preserving reversal median problem (pRMP). A tree-based
                 data structure for the representation of the common
                 intervals of all input gene orders is used in our exact
                 algorithm TCIP for solving the pRMP. It is known that
                 the minimum number of reversals to transform one gene
                 order into another can be computed in polynomial time,
                 whereas the corresponding problem with the restriction
                 that common intervals should not be destroyed is
                 already NP-hard. It is shown theoretically that TCIP
                 can solve a large class of pRMP instances in polynomial
                 time. Empirically we show the good performance of TCIP
                 on biological and artificial data.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "biology and genetics; permutations and combinations",
}

@Article{Braga:2008:ESS,
  author =       "Mar{\'\i}lia D. V. Braga and Marie-France Sagot and
                 Celine Scornavacca and Eric Tannier",
  title =        "Exploring the Solution Space of Sorting by Reversals,
                 with Experiments and an Application to Evolution",
  journal =      j-TCBB,
  volume =       "5",
  number =       "3",
  pages =        "348--356",
  month =        jul,
  year =         "2008",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.16",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Oct 10 12:59:44 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "In comparative genomics, algorithms that sort
                 permutations by reversals are often used to propose
                 evolutionary scenarios of rearrangements between
                 species. One of the main problems of such methods is
                 that they give one solution while the number of optimal
                 solutions is huge, with no criteria to discriminate
                 among them. Bergeron et al. started to give some
                 structure to the set of optimal solutions, in order to
                 be able to deliver more presentable results than only
                 one solution or a complete list of all solutions.
                 However, no algorithm exists so far to compute this
                 structure except through the enumeration of all
                 solutions, which takes too much time even for small
                 permutations. Bergeron et al. state as an open problem
                 the design of such an algorithm. We propose in this
                 paper an answer to this problem, that is, an algorithm
                 which gives all the classes of solutions and counts the
                 number of solutions in each class, with a better
                 theoretical and practical complexity than the complete
                 enumeration method. We give an example of how to reduce
                 the number of classes obtained, using further
                 constraints. Finally, we apply our algorithm to analyse
                 the possible scenarios of rearrangement between
                 mammalian sex chromosomes.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "common intervals; evolution; genome rearrangements;
                 perfect sorting; sex chromosomes; signed permutations;
                 sorting by reversals",
}

@Article{Vassura:2008:RSP,
  author =       "Marco Vassura and Luciano Margara and Pietro {Di Lena}
                 and Filippo Medri and Piero Fariselli and Rita
                 Casadio",
  title =        "Reconstruction of {$3$D} Structures From Protein
                 Contact Maps",
  journal =      j-TCBB,
  volume =       "5",
  number =       "3",
  pages =        "357--367",
  month =        jul,
  year =         "2008",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.27",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Oct 10 12:59:44 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The prediction of the protein tertiary structure from
                 solely its residue sequence (the so called Protein
                 Folding Problem) is one of the most challenging
                 problems in Structural Bioinformatics. We focus on the
                 protein residue contact map. When this map is assigned
                 it is possible to reconstruct the 3D structure of the
                 protein backbone. The general problem of recovering a
                 set of 3D coordinates consistent with some given
                 contact map is known as a unit-disk-graph realization
                 problem and it has been recently proven to be NP-Hard.
                 In this paper we describe a heuristic method (COMAR)
                 that is able to reconstruct with an unprecedented rate
                 (3-15 seconds) a 3D model that exactly matches the
                 target contact map of a protein. Working with a
                 non-redundant set of 1760 proteins, we find that the
                 scoring efficiency of finding a 3D model very close to
                 the protein native structure depends on the threshold
                 value adopted to compute the protein residue contact
                 map. Contact maps whose threshold values range from 10
                 to 18 {\AA}ngstroms allow reconstructing 3D models that
                 are very similar to the proteins native structure.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "combinatorial algorithms; contact map; molecular
                 modeling; protein structure prediction",
}

@Article{Lee:2008:IEN,
  author =       "George Lee and Carlos Rodriguez and Anant Madabhushi",
  title =        "Investigating the Efficacy of Nonlinear Dimensionality
                 Reduction Schemes in Classifying Gene and Protein
                 Expression Studies",
  journal =      j-TCBB,
  volume =       "5",
  number =       "3",
  pages =        "368--384",
  month =        jul,
  year =         "2008",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.36",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Oct 10 12:59:44 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The recent explosion in procurement and availability
                 of high-dimensional gene- and protein-expression
                 profile datasets for cancer diagnostics has
                 necessitated the development of sophisticated machine
                 learning tools with which to analyze them. A major
                 limitation in the ability to accurate classify these
                 high-dimensional datasets stems from the `curse of
                 dimensionality', occurring in situations where the
                 number of genes or peptides significantly exceeds the
                 total number of patient samples. Previous attempts at
                 dealing with this issue have mostly centered on the use
                 of a dimensionality reduction (DR) scheme, Principal
                 Component Analysis (PCA), to obtain a low-dimensional
                 projection of the high-dimensional data. However,
                 linear PCA and other linear DR methods, which rely on
                 Euclidean distances to estimate object similarity, do
                 not account for the inherent underlying nonlinear
                 structure associated with most biomedical data. The
                 motivation behind this work is to identify the
                 appropriate DR methods for analysis of high-dimensional
                 gene- and protein-expression studies. Towards this end,
                 we empirically and rigorously compare three nonlinear
                 (Isomap, Locally Linear Embedding, Laplacian Eigenmaps)
                 and three linear DR schemes (PCA, Linear Discriminant
                 Analysis, Multidimensional Scaling) with the intent of
                 determining a reduced subspace representation in which
                 the individual object classes are more easily
                 discriminable.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "and association rules; Bioinformatics (genome or
                 protein) databases; classification; clustering; data
                 and knowledge visualization; data mining; feature
                 extraction or construction",
}

@Article{Cho:2008:CHC,
  author =       "Hyuk Cho and Inderjit S. Dhillon",
  title =        "Coclustering of Human Cancer Microarrays Using Minimum
                 Sum-Squared Residue Coclustering",
  journal =      j-TCBB,
  volume =       "5",
  number =       "3",
  pages =        "385--400",
  month =        jul,
  year =         "2008",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2007.70268",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Oct 10 12:59:44 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "It is a consensus in microarray analysis that
                 identifying potential local patterns, characterized by
                 coherent groups of genes and conditions, may shed light
                 on the discovery of previously undetectable biological
                 cellular processes of genes as well as macroscopic
                 phenotypes of related samples. In order to
                 simultaneously cluster genes and conditions, we have
                 previously developed a fast co-clustering algorithm,
                 Minimum Sum-Squared Residue Co-clustering (MSSRCC),
                 which employs an alternating minimization scheme and
                 generates what we call co-clusters in a checkerboard
                 structure. In this paper, we propose specific
                 strategies that enable MSSRCC to escape poor local
                 minima and resolve the degeneracy problem in
                 partitional clustering algorithms. The strategies
                 include binormalization, deterministic spectral
                 initialization, and incremental local search. We assess
                 the effects of various strategies on both synthetic
                 gene expression datasets and real human cancer
                 microarrays and provide empirical evidence that MSSRCC
                 with the proposed strategies performs better than
                 existing co-clustering and clustering algorithms. In
                 particular, the combination of all the three strategies
                 leads to the best performance. Furthermore, we
                 illustrate coherence of the resulting co-clusters in a
                 checkerboard structure, where genes in a co-cluster
                 manifest the phenotype structure of corresponding
                 specific samples, and evaluate the enrichment of
                 functional annotations in Gene Ontology (GO).",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "binormalization; co-clustering; deterministic spectral
                 initialization; gene ontology; local search; microarray
                 analysis",
}

@Article{Wei:2008:IGF,
  author =       "Peng Wei and Wei Pan",
  title =        "Incorporating Gene Functions into Regression Analysis
                 of {DNA}-Protein Binding Data and Gene Expression Data
                 to Construct Transcriptional Networks",
  journal =      j-TCBB,
  volume =       "5",
  number =       "3",
  pages =        "401--415",
  month =        jul,
  year =         "2008",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2007.1062",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Oct 10 12:59:44 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Useful information on transcriptional networks has
                 been extracted by regression analyses of gene
                 expression data and DNA-protein binding data. However,
                 a potential limitation of these approaches is their
                 assumption on the common and constant activity level of
                 a transcription factor (TF) on all the genes in any
                 given experimental condition; for example, any TF is
                 assumed to be either an activator or a repressor, but
                 not both, while it is known that some TFs can be dual
                 regulators. Rather than assuming a common linear
                 regression model for all the genes, we propose using
                 separate regression models for various gene groups; the
                 genes can be grouped based on their functions or some
                 clustering results. Furthermore, to take advantage of
                 the hierarchical structure of many existing gene
                 function annotation systems, such as Gene Ontology
                 (GO), we propose a shrinkage method that borrows
                 information from relevant gene groups. Applications to
                 a yeast dataset and simulations lend support for our
                 proposed methods. In particular, we find that the
                 shrinkage method consistently works well under various
                 scenarios. We recommend the use of the shrinkage method
                 as a useful alternative to the existing methods.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "LASSO; microarray; shrinkage estimator; stratified
                 analysis; transcription factor",
}

@Article{Mak:2008:PPS,
  author =       "Man-Wai Mak and Jian Guo and Sun-Yuan Kung",
  title =        "{PairProSVM}: Protein Subcellular Localization Based
                 on Local Pairwise Profile Alignment and {SVM}",
  journal =      j-TCBB,
  volume =       "5",
  number =       "3",
  pages =        "416--422",
  month =        jul,
  year =         "2008",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2007.70256",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Oct 10 12:59:44 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The subcellular locations of proteins are important
                 functional annotations. An effective and reliable
                 subcellular localization method is necessary for
                 proteomics research. This paper introduces a new
                 method---PairProSVM---to automatically predict the
                 subcellular locations of proteins. The profiles of all
                 protein sequences in the training set are constructed
                 by PSI-BLAST and the pairwise profile-alignment scores
                 are used to form feature vectors for training a support
                 vector machine (SVM) classifier. It was found that
                 PairProSVM outperforms the methods that are based on
                 sequence alignment and amino-acid compositions even if
                 most of the homologous sequences have been removed.
                 This paper also demonstrates that the performance of
                 PairProSVM is sensitive (and somewhat proportional) to
                 the degree of its kernel matrix meeting the Mercer's
                 condition. PairProSVM was evaluated on Reinhardt and
                 Hubbard's, Huang and Li's, and Gardy et al.'s protein
                 datasets. The overall accuracies on these three
                 datasets reach 99.3\%, 76.5\%, and 91.9\%,
                 respectively, which are higher than or comparable to
                 those obtained by sequence alignment and by the methods
                 compared in this paper.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "kernel methods; Mercer condition; profile alignment;
                 subcellular localization; support vector machines",
}

@Article{Elo:2008:ROT,
  author =       "Laura L. Elo and Sanna Filen and Riitta Lahesmaa and
                 Tero Aittokallio",
  title =        "Reproducibility-Optimized Test Statistic for Ranking
                 Genes in Microarray Studies",
  journal =      j-TCBB,
  volume =       "5",
  number =       "3",
  pages =        "423--431",
  month =        jul,
  year =         "2008",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/tcbb.2007.1078",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Oct 10 12:59:44 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "A principal goal of microarray studies is to identify
                 the genes showing differential expression under
                 distinct conditions. In such studies, the selection of
                 an optimal test statistic is a crucial challenge, which
                 depends on the type and amount of data under analysis.
                 While previous studies on simulated or spike-in
                 datasets do not provide practical guidance on how to
                 choose the best method for a given real dataset, we
                 introduce an enhanced reproducibility-optimization
                 procedure, which enables the selection of a suitable
                 gene- anking statistic directly from the data. In
                 comparison with existing ranking methods, the
                 reproducibility-optimized statistic shows good
                 performance consistently under various simulated
                 conditions and on Affymetrix spike-in dataset. Further,
                 the feasibility of the novel statistic is confirmed in
                 a practical research setting using data from an
                 in-house cDNA microarray study of asthma-related gene
                 expression changes. These results suggest that the
                 procedure facilitates the selection of an appropriate
                 test statistic for a given dataset without relying on a
                 priori assumptions, which may bias the findings and
                 their interpretation. Moreover, the general
                 reproducibility-optimization procedure is not limited
                 to detecting differential expression only but could be
                 extended to a wide range of other applications as
                 well.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "bootstrap; differential expression; gene expression;
                 gene ranking; microarray; reproducibility",
}

@Article{Parker:2008:SPT,
  author =       "Douglass Stott Parker and Ruey-Lung Hsiao and Yi Xing
                 and Alissa M. Resch and Christopher J. Lee",
  title =        "Solving the Problem of Trans-Genomic Query with
                 Alignment Tables",
  journal =      j-TCBB,
  volume =       "5",
  number =       "3",
  pages =        "432--447",
  month =        jul,
  year =         "2008",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2007.1073",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Oct 10 12:59:44 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The trans-genomic query (TGQ) problem -- enabling the
                 free query of biological information, even across
                 genomes -- is a central challenge facing
                 bioinformatics. Solutions to this problem can alter the
                 nature of the field, moving it beyond the jungle of
                 data integration and expanding the number and scope of
                 questions that can be answered. An alignment table is a
                 binary relationship on locations (sequence segments).
                 An important special case of alignment tables are hit
                 tables ? tables of pairs of highly similar segments
                 produced by alignment tools like BLAST. However,
                 alignment tables also include general binary
                 relationships, and can represent any useful connection
                 between sequence locations. They can be curated, and
                 provide a high-quality queryable backbone of
                 connections between biological information. Alignment
                 tables thus can be a natural foundation for TGQ, as
                 they permit a central part of the TGQ problem to be
                 reduced to purely technical problems involving tables
                 of locations. Key challenges in implementing alignment
                 tables include efficient representation and indexing of
                 sequence locations. We define a location datatype that
                 can be incorporated naturally into common off-the-shelf
                 database systems. We also describe an implementation of
                 alignment tables in BLASTGRES, an extension of the
                 open-source POSTGRESQL database system that provides
                 indexing and operators on locations required for
                 querying alignment tables. This paper also reviews
                 several successful large-scale applications of
                 alignment tables for Trans-Genomic Query. Tables with
                 millions of alignments have been used in queries about
                 alternative splicing, an area of genomic analysis
                 concerning the way in which a single gene can yield
                 multiple transcripts. Comparative genomics is a large
                 potential application area for TGQ and alignment
                 tables.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Dawy:2008:FSG,
  author =       "Zaher Dawy and Michel Sarkis and Joachim Hagenauer and
                 Jakob C. Mueller",
  title =        "Fine-Scale Genetic Mapping Using Independent Component
                 Analysis",
  journal =      j-TCBB,
  volume =       "5",
  number =       "3",
  pages =        "448--460",
  month =        jul,
  year =         "2008",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2007.1072",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Oct 10 12:59:44 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The aim of genetic mapping is to locate the loci
                 responsible for specific traits such as complex
                 diseases. These traits are normally caused by mutations
                 at multiple loci of unknown locations and interactions.
                 In this work, we model the biological system that
                 relates DNA polymorphisms with complex traits as a
                 linear mixing process. Given this model, we propose a
                 new fine-scale genetic mapping method based on
                 independent component analysis. The proposed method
                 outputs both independent associated groups of SNPs in
                 addition to specific associated SNPs with the
                 phenotype. It is applied to a clinical data set for the
                 Schizophrenia disease with 368 individuals and 42 SNPs.
                 It is also applied to a simulation study to investigate
                 in more depth its performance. The obtained results
                 demonstrate the novel characteristics of the proposed
                 method compared to other genetic mapping methods.
                 Finally, we study the robustness of the proposed method
                 with missing genotype values and limited sample
                 sizes.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "association mapping; complex diseases; independent
                 component analysis (ICA); linkage disequilibrium;
                 principal component analysis (PCA); single nucleotide
                 polymorphisms (SNPs)",
}

@Article{Hendy:2008:HCK,
  author =       "Michael D. Hendy and Sagi Snir",
  title =        "{Hadamard} Conjugation for the {Kimura} {3ST} Model:
                 Combinatorial Proof Using Path Sets",
  journal =      j-TCBB,
  volume =       "5",
  number =       "3",
  pages =        "461--471",
  month =        jul,
  year =         "2008",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2007.70227",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Oct 10 12:59:44 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Under a stochastic model of molecular sequence
                 evolution the probability of each possible pattern of a
                 characters is well defined. The Kimura's
                 three-substitution-types (K3ST) model of evolution,
                 allows analytical expression for these probabilities of
                 by means of the Hadamard conjugation as a function of
                 the phylogeny T and the substitution probabilities on
                 each edge of TM. In this paper we produce a direct
                 combinatorial proof of these results, using pathset
                 distances which generalise pairwise distances between
                 sequences. This interpretation provides us with tools
                 that were proved useful in related problems in the
                 mathematical analysis of sequence evolution.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "Hadamard conjugation; K3ST model; path-sets;
                 phylogenetic invariants; phylogenetic trees",
}

@Article{Gambette:2008:ILP,
  author =       "Philippe Gambette and Daniel H. Huson",
  title =        "Improved Layout of Phylogenetic Networks",
  journal =      j-TCBB,
  volume =       "5",
  number =       "3",
  pages =        "472--479",
  month =        jul,
  year =         "2008",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/tcbb.2007.1046",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Oct 10 12:59:44 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Split networks are increasingly being used in
                 phylogenetic analysis. Usually, a simple equal angle
                 algorithm is used to draw such networks, producing
                 layouts that leave much room for improvement.
                 Addressing the problem of producing better layouts of
                 split networks, this paper presents an algorithm for
                 maximizing the area covered by the network, describes
                 an extension of the equal-daylight algorithm to
                 networks, looks into using a spring embedder and
                 discusses how to construct rooted split networks.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "algorithms; graph drawing; phylogenetic networks;
                 phylogenetics",
}

@Article{Gusfield:2008:EE,
  author =       "Dan Gusfield",
  title =        "{EIC} Editorial",
  journal =      j-TCBB,
  volume =       "5",
  number =       "4",
  pages =        "481--481",
  month =        oct,
  year =         "2008",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.115",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 14 12:51:33 MST 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Giancarlo:2008:GEI,
  author =       "Raffaele Giancarlo and Sridhar Hannenhalli",
  title =        "{Guest Editors}' Introduction to the Special Section
                 on Algorithms in Bioinformatics",
  journal =      j-TCBB,
  volume =       "5",
  number =       "4",
  pages =        "482--483",
  month =        oct,
  year =         "2008",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.116",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 14 12:51:33 MST 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Jeong:2008:ISP,
  author =       "Jieun Jeong and Piotr Berman and Teresa M. Przytycka",
  title =        "Improving Strand Pairing Prediction through Exploring
                 Folding Cooperativity",
  journal =      j-TCBB,
  volume =       "5",
  number =       "4",
  pages =        "484--491",
  month =        oct,
  year =         "2008",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.88",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 14 12:51:33 MST 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The topology of $ \beta $-sheets is defined by the
                 pattern of hydrogen-bonded strand pairing. Therefore,
                 predicting hydrogen bonded strand partners is a
                 fundamental step towards predicting $ \beta $-sheet
                 topology. At the same time, finding the correct
                 partners is very difficult due to long range
                 interactions involved in strand pairing. Additionally,
                 patterns of aminoacids involved, in $ \beta $-sheet
                 formations are very general and therefore difficult to
                 use for computational recognition of specific contacts
                 between strands. In this work, we report a new strand
                 pairing algorithm. To address above mentioned
                 difficulties, our algorithm attempts to mimic elements
                 of the folding process. Namely, in addition to ensuring
                 that the predicted hydrogen bonded strand pairs satisfy
                 basic global consistency constraints, it takes into
                 account hypothetical folding pathways. Consistently
                 with this view, introducing hydrogen bonds between a
                 pair of strands changes the probabilities of forming
                 hydrogen bonds between other pairs of strand. We
                 demonstrate that this approach provides an improvement
                 over previously proposed algorithms. We also compare
                 the performance of this method to that of a global
                 optimization algorithm that poses the problem as
                 integer linear programming optimization problem and
                 solves it using ILOG CPLEX\TM{} package.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "Biology and genetics; Combinatorial algorithms",
}

@Article{Genovese:2008:SAH,
  author =       "Loredana M. Genovese and Filippo Geraci and Marco
                 Pellegrini",
  title =        "{SpeedHap}: An Accurate Heuristic for the Single
                 Individual {SNP} Haplotyping Problem with Many Gaps,
                 High Reading Error Rate and Low Coverage",
  journal =      j-TCBB,
  volume =       "5",
  number =       "4",
  pages =        "492--502",
  month =        oct,
  year =         "2008",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.67",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 14 12:51:33 MST 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Single nucleotide polymorphism (SNP) is the most
                 frequent form of DNA variation. The set of SNP's
                 present in a chromosome (called the em haplotype) is of
                 interest in a wide area of applications in molecular
                 biology and biomedicine, including diagnostic and
                 medical therapy. In this paper we propose a new
                 heuristic method for the problem of haplotype
                 reconstruction for (portions of) a pair of homologous
                 human chromosomes from a single individual (SIH). The
                 problem is well known in literature and exact
                 algorithms have been proposed for the case when no (or
                 few) gaps are allowed in the input fragments. These
                 algorithms, though exact and of polynomial complexity,
                 are slow in practice. When gaps are considered no exact
                 method of polynomial complexity is known. The problem
                 is also hard to approximate with guarantees. Therefore
                 fast heuristics have been proposed. In this paper we
                 describe SpeedHap, a new heuristic method that is able
                 to tackle the case of many gapped fragments and retains
                 its effectiveness even when the input fragments have
                 high rate of reading errors (up to 20\%) and low
                 coverage (as low as 3). We test SpeedHap on real data
                 from the HapMap Project.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "Algorithms; Biology and genetics",
}

@Article{Lozano:2008:STA,
  author =       "Antoni Lozano and Ron Y. Pinter and Oleg Rokhlenko and
                 Gabriel Valiente and Michal Ziv-Ukelson",
  title =        "Seeded Tree Alignment",
  journal =      j-TCBB,
  volume =       "5",
  number =       "4",
  pages =        "503--513",
  month =        oct,
  year =         "2008",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.59",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 14 12:51:33 MST 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The optimal transformation of one tree into another by
                 means of elementary edit operations is an important
                 algorithmic problem that has several interesting
                 applications to computational biology. Here we
                 introduce a constrained form of this problem in which a
                 partial mapping of a set of nodes (the `seeds') in one
                 tree to a corresponding set of nodes in the other tree
                 is given, and present efficient algorithms for both
                 ordered and unordered trees. Whereas ordered tree
                 matching based on seeded nodes has applications in
                 pattern matching of RNA structures, unordered tree
                 matching based on seeded nodes has applications in
                 co-speciation and phylogeny reconciliation. The latter
                 involves the solution of the planar tanglegram layout
                 problem, for which a polynomial-time algorithm is given
                 here.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "Biology and genetics; Computer Applications; Discrete
                 Mathematics; Graph algorithms; Graph Theory; Life and
                 Medical Sciences; Mathematics of Computing; Trees",
}

@Article{Bansal:2008:STH,
  author =       "Mukul S. Bansal and Oliver Eulenstein",
  title =        "An {$ \Omega (n^2 / \log n) $} Speed-Up of {TBR}
                 Heuristics for the Gene-Duplication Problem",
  journal =      j-TCBB,
  volume =       "5",
  number =       "4",
  pages =        "514--524",
  month =        oct,
  year =         "2008",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.69",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 14 12:51:33 MST 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The gene-duplication problem is to infer a species
                 supertree from gene trees that are confounded by
                 complex histories of gene duplications. This problem is
                 NP-hard and thus requires efficient and effective
                 heuristics. Existing heuristics perform a stepwise
                 search of the tree space, where each step is guided by
                 an exact solution to an instance of a local search
                 problem. We improve on the time complexity of the local
                 search problem by a factor of $ n^2 = \log n $, where
                 $n$ is the size of the resulting species supertree.
                 Typically, several thousand instances of the local
                 search problem are solved throughout a stepwise
                 heuristic search. Hence, our improvement makes the
                 gene-duplication problem much more tractable for
                 large-scale phylogenetic analyses.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "Algorithms; Computational Biology; Gene Duplication;
                 Phylogenetics; Supertrees",
}

@Article{Wang:2008:DCO,
  author =       "Xueyi Wang and Jack Snoeyink",
  title =        "Defining and Computing Optimum {RMSD} for Gapped and
                 Weighted Multiple-Structure Alignment",
  journal =      j-TCBB,
  volume =       "5",
  number =       "4",
  pages =        "525--533",
  month =        oct,
  year =         "2008",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.92",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 14 12:51:33 MST 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Pairwise structure alignment commonly uses root mean
                 square deviation (RMSD) to measure the structural
                 similarity, and methods for optimizing RMSD are well
                 established. We extend RMSD to weighted RMSD for
                 multiple structures. By using multiplicative weights,
                 we show that weighted RMSD for all pairs is the same as
                 weighted RMSD to an average of the structures. Thus,
                 using RMSD or weighted RMSD implies that the average is
                 a consensus structure. Although we show that in
                 general, the two tasks of finding the optimal
                 translations and rotations for minimizing weighted RMSD
                 cannot be separated for multiple structures like they
                 can for pairs, an inherent difficulty and a fact
                 ignored by previous work, we develop a near-linear
                 iterative algorithm to converge weighted RMSD to a
                 local minimum. 10,000 experiments of gapped alignment
                 done on each of 23 protein families from HOMSTRAD
                 (where each structure starts with a random translation
                 and rotation) converge rapidly to the same minimum.
                 Finally we propose a heuristic method to iteratively
                 remove the effect of outliers and find well-aligned
                 positions that determine the structural conserved
                 region by modeling B-factors and deviations from the
                 average positions as weights and iteratively assigning
                 higher weights to better aligned atoms.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "multiple structure alignment; optimization methods;
                 structural conserved region; weighted RMSD",
}

@Article{Yao:2008:EAE,
  author =       "Peggy Yao and Ankur Dhanik and Nathan Marz and Ryan
                 Propper and Charles Kou and Guanfeng Liu and Henry van
                 den Bedem and Jean-Claude Latombe and Inbal
                 Halperin-Landsberg and Russ B. Altman",
  title =        "Efficient Algorithms to Explore Conformation Spaces of
                 Flexible Protein Loops",
  journal =      j-TCBB,
  volume =       "5",
  number =       "4",
  pages =        "534--545",
  month =        oct,
  year =         "2008",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.96",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 14 12:51:33 MST 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Several applications in biology --- e.g.,
                 incorporation of protein flexibility in ligand docking
                 algorithms, interpretation of fuzzy X-ray
                 crystallographic data, and homology modeling ---
                 require computing the internal parameters of a flexible
                 fragment (usually, a loop) of a protein in order to
                 connect its termini to the rest of the protein without
                 causing any steric clash. One must often sample many
                 such conformations in order to explore and adequately
                 represent the conformational range of the studied loop.
                 While sampling must be fast, it is made difficult by
                 the fact that two conflicting constraints --- kinematic
                 closure and clash avoidance --- must be satisfied
                 concurrently. This paper describes two efficient and
                 complementary sampling algorithms to explore the space
                 of closed clash-free conformations of a flexible
                 protein loop. The `seed sampling' algorithm samples
                 broadly from this space, while the `deformation
                 sampling' algorithm uses seed conformations as starting
                 points to explore the conformation space around them at
                 a finer grain. Computational results are presented for
                 various loops ranging from 5 to 25 residues. More
                 specific results also show that the combination of the
                 sampling algorithms with a functional site prediction
                 software (FEATURE) makes it possible to compute and
                 recognize calcium-binding loop conformations. The
                 sampling algorithms are implemented in a toolkit
                 (LoopTK), which is available at
                 \path=https://simtk.org/home/looptk=.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "Biology and genetics; Robotics",
}

@Article{Kim:2008:LSS,
  author =       "Eagu Kim and John Kececioglu",
  title =        "Learning Scoring Schemes for Sequence Alignment from
                 Partial Examples",
  journal =      j-TCBB,
  volume =       "5",
  number =       "4",
  pages =        "546--556",
  month =        oct,
  year =         "2008",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.57",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 14 12:51:33 MST 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "When aligning biological sequences, the choice of
                 parameter values for the alignment scoring function is
                 critical. Small changes in gap penalties, for example,
                 can yield radically different alignments. A rigorous
                 way to compute parameter values that are appropriate
                 for aligning biological sequences is through inverse
                 parametric sequence alignment. Given a collection of
                 examples of biologically correct alignments, this is
                 the problem of finding parameter values that make the
                 scores of the example alignments close to those of
                 optimal alignments for their sequences. We extend prior
                 work on inverse parametric alignment to partial
                 examples, which contain regions where the alignment is
                 left unspecified, and to an improved formulation based
                 on minimizing the average error between the score of an
                 example and the score of an optimal alignment.
                 Experiments on benchmark biological alignments show we
                 can find parameters that generalize across protein
                 families and that boost the accuracy of multiple
                 sequence alignment by as much as 25\%.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "Analysis of Algorithms and Problem Complexity; Biology
                 and genetics; Linear programming; Pattern matching",
}

@Article{Schliep:2008:EAC,
  author =       "Alexander Schliep and Roland Krause",
  title =        "Efficient Algorithms for the Computational Design of
                 Optimal Tiling Arrays",
  journal =      j-TCBB,
  volume =       "5",
  number =       "4",
  pages =        "557--567",
  month =        oct,
  year =         "2008",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.50",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 14 12:51:33 MST 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The representation of a genome by oligonucleotide
                 probes is a prerequisite for the analysis of many of
                 its basic properties, such as transcription factor
                 binding sites, chromosomal breakpoints, gene expression
                 of known genes and detection of novel genes, in
                 particular those coding for small RNAs. An ideal
                 representation would consist of a high density set of
                 oligonucleotides with similar melting temperatures that
                 do not cross-hybridize with other regions of the genome
                 and are equidistantly spaced. The implementation of
                 such design is typically called a tiling array or
                 genome array. We formulate the minimal cost tiling path
                 problem for the selection of oligonucleotides from a
                 set of candidates. Computing the selection of probes
                 requires multi-criterion optimization, which we cast
                 into a shortest path problem. Standard algorithms
                 running in linear time allow us to compute globally
                 optimal tiling paths from millions of candidate
                 oligonucleotides on a standard desktop computer for
                 most problem variants. The solutions to this
                 multi-criterion optimization are spatially adaptive to
                 the problem instance. Our formulation incorporates
                 experimental constraints with respect to specific
                 regions of interest and trade offs between
                 hybridization parameters, probe quality and tiling
                 density easily.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "Biology and genetics; Graph Theory",
}

@Article{Yu:2008:CAA,
  author =       "Zeyun Yu and Chandrajit Bajaj",
  title =        "Computational Approaches for Automatic Structural
                 Analysis of Large Biomolecular Complexes",
  journal =      j-TCBB,
  volume =       "5",
  number =       "4",
  pages =        "568--582",
  month =        oct,
  year =         "2008",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2007.70226",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 14 12:51:33 MST 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "We present computational solutions to two problems of
                 macromolecular structure interpretation from
                 reconstructed three-dimensional electron microscopy
                 (3D-EM) maps of large bio-molecular complexes at
                 intermediate resolution (5A-15A). The two problems
                 addressed are: (a) 3D structural alignment
                 (matching)between identified and segmented 3D maps of
                 structure units(e.g. trimeric configuration of
                 proteins), and (b) the secondary structure
                 identification of a segmented protein 3D map (i.e.,
                 locations of $ \alpha $-helices, $ \beta $-sheets). For
                 problem (a), we present an efficient algorithm to
                 correlate spatially (and structurally)two 3D maps of
                 structure units. Besides providing a similarity score
                 between structure units, the algorithm yields an
                 effective technique for resolution refinement of
                 repeated structure units,by 3D alignment and averaging.
                 For problem (b), we present an efficient algorithm to
                 compute eigenvalues and link eigenvectors of a Gaussian
                 convoluted structure tensor derived from the protein 3D
                 Map, thereby identifying and locating secondary
                 structural motifs of proteins. The efficiency and
                 performance of our approach is demonstrated on several
                 experimentally reconstructed 3D maps of virus capsid
                 shells from single-particle cryo-EM, as well as
                 computationally simulated protein structure density 3D
                 maps generated from protein model entries in the
                 Protein Data Bank.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "3D Reconstruction; Alignment; Cryo-EM Maps; Secondary
                 Structure Detection; Segmentation; Similarity Measure;
                 Skeletonization; Structure Analysis",
}

@Article{Christinat:2008:GED,
  author =       "Yann Christinat and Bernd Wachmann and Lei Zhang",
  title =        "Gene Expression Data Analysis Using a Novel Approach
                 to Biclustering Combining Discrete and Continuous
                 Data",
  journal =      j-TCBB,
  volume =       "5",
  number =       "4",
  pages =        "583--593",
  month =        oct,
  year =         "2008",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2007.70251",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 14 12:51:33 MST 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Many different methods exist for pattern detection in
                 gene expression data. In contrast to classical methods,
                 biclustering has the ability to cluster a group of
                 genes together with a group of conditions (replicates,
                 set of patients or drug compounds). However, since the
                 problem is NP-complex, most algorithms use heuristic
                 search functions and therefore might converge towards
                 local maxima. By using the results of biclustering on
                 discrete data as a starting point for a local search
                 function on continuous data, our algorithm avoids the
                 problem of heuristic initialization. Similar to OPSM,
                 our algorithm aims to detect biclusters whose rows and
                 columns can be ordered such that row values are growing
                 across the bicluster's columns and vice-versa. Results
                 have been generated on the yeast genome (Saccharomyces
                 cerevisiae), a human cancer dataset and random data.
                 Results on the yeast genome showed that 89\% of the one
                 hundred biggest non-overlapping biclusters were
                 enriched with Gene Ontology annotations. A comparison
                 with OPSM and ISA demonstrated a better efficiency when
                 using gene and condition orders. We present results on
                 random and real datasets that show the ability of our
                 algorithm to capture statistically significant and
                 biologically relevant biclusters.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "Bioinformatics (genome or protein) databases; Data and
                 knowledge visualization; Data mining; Graph and tree
                 search strategies; Machine learning",
}

@Article{Lacroix:2008:IMN,
  author =       "Vincent Lacroix and Ludovic Cottret and Patricia
                 Th{\'e}bault and Marie-France Sagot",
  title =        "An Introduction to Metabolic Networks and Their
                 Structural Analysis",
  journal =      j-TCBB,
  volume =       "5",
  number =       "4",
  pages =        "594--617",
  month =        oct,
  year =         "2008",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.79",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 14 12:51:33 MST 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "There has been a renewed interest for metabolism in
                 the computational biology community, leading to an
                 avalanche of papers coming from methodological network
                 analysis as well as experimental and theoretical
                 biology. This paper is meant to serve as an initial
                 guide for both the biologists interested in formal
                 approaches and the mathematicians or computer
                 scientists wishing to inject more realism into their
                 models. The paper is focused on the structural aspects
                 of metabolism only. The literature is vast enough
                 already, and the thread through it difficult to follow
                 even for the more experienced worker in the field. We
                 explain methods for acquiring data and reconstructing
                 metabolic networks, and review the various models that
                 have been used for their structural analysis. Several
                 concepts such as modularity are introduced, as are the
                 controversies that have beset the field these past few
                 years, for instance, on whether metabolic networks are
                 small-world or scale-free, and on which model better
                 explains the evolution of metabolism. Clarifying the
                 work that has been done also helps in identifying open
                 questions and in proposing relevant future directions
                 in the field, which we do along the paper and in the
                 conclusion.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "Biology and genetics; evolution; Graph Theory;
                 Introductory and Survey; metabolic networks; modelling;
                 modularity; reconstruction",
}

@Article{Satya:2008:UIP,
  author =       "Ravi Vijaya Satya and Amar Mukherjee",
  title =        "The Undirected Incomplete Perfect Phylogeny Problem",
  journal =      j-TCBB,
  volume =       "5",
  number =       "4",
  pages =        "618--629",
  month =        oct,
  year =         "2008",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2007.70218",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 14 12:51:33 MST 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The incomplete perfect phylogeny (IPP) problem and the
                 incomplete perfect phylogeny haplotyping (IPPH) problem
                 deal with constructing a phylogeny for a given set of
                 haplotypes or genotypes with missing entries. The
                 earlier approaches for both of these problems dealt
                 with restricted versions of the problems, where the
                 root is either available or can be trivially
                 re-constructed from the data, or certain assumptions
                 were made about the data. In this paper, we deal with
                 the unrestricted versions of the problems, where the
                 root of the phylogeny is neither available nor
                 trivially recoverable from the data. Both IPP and IPPH
                 problems have previously been proven to be NP complete.
                 Here, we present efficient enumerative algorithms that
                 can handle practical instances of the problem.
                 Empirical analysis on simulated data shows that the
                 algorithms perform very well both in terms of speed and
                 in terms accuracy of the recovered data.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "Haplotype Inference; Incomplete Perfect Phylogeny;
                 Perfect Phylogeny; Phylogenetics",
}

@Article{Gondro:2008:OCM,
  author =       "Cedric Gondro and Brian P. Kinghorn",
  title =        "Optimization of {cDNA} Microarray Experimental Designs
                 Using an Evolutionary Algorithm",
  journal =      j-TCBB,
  volume =       "5",
  number =       "4",
  pages =        "630--638",
  month =        oct,
  year =         "2008",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2007.70222",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 14 12:51:33 MST 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The cDNA microarray is an important tool for
                 generating large datasets of gene expression
                 measurements. An efficient design is critical to ensure
                 that the experiment will be able to address relevant
                 biological questions. Microarray experimental design
                 can be treated as a multicriterion optimization
                 problem. For this class of problems evolutionary
                 algorithms (EAs) are well suited, as they can search
                 the solution space and evolve a design that optimizes
                 the parameters of interest based on their relative
                 value to the researcher under a given set of
                 constraints. This paper introduces the use of EAs for
                 optimization of experimental designs of spotted
                 microarrays using a weighted objective function. The EA
                 and the various criteria relevant to design
                 optimization are discussed. Evolved designs are
                 compared with designs obtained through exhaustive
                 search with results suggesting that the EA can find
                 just as efficient optimal or near-optimal designs
                 within a tractable timeframe.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "Evolutionary computing and genetic algorithms;
                 experimental design; global optimization; microarrays",
}

@Article{Gusfield:2009:FFY,
  author =       "Dan Gusfield",
  title =        "Final, Five-Year End, Editorial",
  journal =      j-TCBB,
  volume =       "6",
  number =       "1",
  pages =        "1--2",
  month =        jan,
  year =         "2009",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 2 18:46:49 MST 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Sagot:2009:NEE,
  author =       "Marie-France Sagot",
  title =        "New {EIC} Editorial",
  journal =      j-TCBB,
  volume =       "6",
  number =       "1",
  pages =        "3--3",
  month =        jan,
  year =         "2009",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 2 18:46:49 MST 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Huson:2009:SSP,
  author =       "Daniel H. Huson and Vincent Moulton and Mike Steel",
  title =        "Special Section: Phylogenetics",
  journal =      j-TCBB,
  volume =       "6",
  number =       "1",
  pages =        "4--6",
  month =        jan,
  year =         "2009",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 2 18:46:49 MST 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Liu:2009:BWT,
  author =       "Kevin Liu and Serita Nelesen and Sindhu Raghavan and
                 C. Randal Linder and Tandy Warnow",
  title =        "Barking Up The Wrong Treelength: The Impact of Gap
                 Penalty on Alignment and Tree Accuracy",
  journal =      j-TCBB,
  volume =       "6",
  number =       "1",
  pages =        "7--21",
  month =        jan,
  year =         "2009",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 2 18:46:49 MST 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Minh:2009:BPD,
  author =       "Bui Quang Minh and Fabio Pardi and Steffen Klaere and
                 Arndt von Haeseler",
  title =        "Budgeted Phylogenetic Diversity on Circular Split
                 Systems",
  journal =      j-TCBB,
  volume =       "6",
  number =       "1",
  pages =        "22--29",
  month =        jan,
  year =         "2009",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 2 18:46:49 MST 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Linz:2009:HNT,
  author =       "Simone Linz and Charles Semple",
  title =        "Hybridization in Nonbinary Trees",
  journal =      j-TCBB,
  volume =       "6",
  number =       "1",
  pages =        "30--45",
  month =        jan,
  year =         "2009",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 2 18:46:49 MST 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Cardona:2009:MPNa,
  author =       "Gabriel Cardona and Merc{\`e} Llabr{\'e}s and Francesc
                 Rossell{\'o} and Gabriel Valiente",
  title =        "Metrics for Phylogenetic Networks {I}: Generalizations
                 of the {Robinson--Foulds} Metric",
  journal =      j-TCBB,
  volume =       "6",
  number =       "1",
  pages =        "46--61",
  month =        jan,
  year =         "2009",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 2 18:46:49 MST 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Willson:2009:RTS,
  author =       "Stephen J. Willson",
  title =        "Robustness of Topological Supertree Methods for
                 Reconciling Dense Incompatible Data",
  journal =      j-TCBB,
  volume =       "6",
  number =       "1",
  pages =        "62--75",
  month =        jan,
  year =         "2009",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 2 18:46:49 MST 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Allman:2009:ICM,
  author =       "Elizabeth S. Allman and John A. Rhodes",
  title =        "The Identifiability of Covarion Models in
                 Phylogenetics",
  journal =      j-TCBB,
  volume =       "6",
  number =       "1",
  pages =        "76--88",
  month =        jan,
  year =         "2009",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 2 18:46:49 MST 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Matsen:2009:FTI,
  author =       "Frederick A. Matsen",
  title =        "{Fourier} Transform Inequalities for Phylogenetic
                 Trees",
  journal =      j-TCBB,
  volume =       "6",
  number =       "1",
  pages =        "89--95",
  month =        jan,
  year =         "2009",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 2 18:46:49 MST 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Gusfield:2009:OEE,
  author =       "Dan Gusfield",
  title =        "Outgoing {EIC} Editorial for this Special Section of
                 {TCBB} with the Theme of Phylogenetics",
  journal =      j-TCBB,
  volume =       "6",
  number =       "1",
  pages =        "96--96",
  month =        jan,
  year =         "2009",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 2 18:46:49 MST 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Grunewald:2009:MPT,
  author =       "Stefan Gr{\"u}newald and Vincent Moulton",
  title =        "Maximum Parsimony for Tree Mixtures",
  journal =      j-TCBB,
  volume =       "6",
  number =       "1",
  pages =        "97--102",
  month =        jan,
  year =         "2009",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 2 18:46:49 MST 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Huson:2009:DRP,
  author =       "Daniel H. Huson",
  title =        "Drawing Rooted Phylogenetic Networks",
  journal =      j-TCBB,
  volume =       "6",
  number =       "1",
  pages =        "103--109",
  month =        jan,
  year =         "2009",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 2 18:46:49 MST 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Bordewich:2009:CTM,
  author =       "Magnus Bordewich and Olivier Gascuel and Katharina T.
                 Huber and Vincent Moulton",
  title =        "Consistency of Topological Moves Based on the Balanced
                 Minimum Evolution Principle of Phylogenetic Inference",
  journal =      j-TCBB,
  volume =       "6",
  number =       "1",
  pages =        "110--117",
  month =        jan,
  year =         "2009",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 2 18:46:49 MST 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Wu:2009:RPT,
  author =       "Taoyang Wu and Vincent Moulton and Mike Steel",
  title =        "Refining Phylogenetic Trees Given Additional Data: An
                 Algorithm Based on Parsimony",
  journal =      j-TCBB,
  volume =       "6",
  number =       "1",
  pages =        "118--125",
  month =        jan,
  year =         "2009",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 2 18:46:49 MST 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Mossel:2009:SEA,
  author =       "Elchanan Mossel and Sebastien Roch and Mike Steel",
  title =        "Shrinkage Effect in Ancestral Maximum Likelihood",
  journal =      j-TCBB,
  volume =       "6",
  number =       "1",
  pages =        "126--133",
  month =        jan,
  year =         "2009",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 2 18:46:49 MST 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Ma:2009:GCU,
  author =       "Jianmin Ma and Minh N. Nguyen and Jagath C.
                 Rajapakse",
  title =        "Gene Classification Using Codon Usage and Support
                 Vector Machines",
  journal =      j-TCBB,
  volume =       "6",
  number =       "1",
  pages =        "134--143",
  month =        jan,
  year =         "2009",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 2 18:46:49 MST 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Maitra:2009:IPO,
  author =       "Ranjan Maitra",
  title =        "Initializing Partition-Optimization Algorithms",
  journal =      j-TCBB,
  volume =       "6",
  number =       "1",
  pages =        "144--157",
  month =        jan,
  year =         "2009",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 2 18:46:49 MST 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Narasimhan:2009:SPG,
  author =       "Sridharakumar Narasimhan and Raghunathan Rengaswamy
                 and Rajanikanth Vadigepalli",
  title =        "Structural Properties of Gene Regulatory Networks:
                 Definitions and Connections",
  journal =      j-TCBB,
  volume =       "6",
  number =       "1",
  pages =        "158--170",
  month =        jan,
  year =         "2009",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 2 18:46:49 MST 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Anonymous:2009:RL,
  author =       "Anonymous",
  title =        "2008 Reviewers List",
  journal =      j-TCBB,
  volume =       "6",
  number =       "1",
  pages =        "171--173",
  month =        jan,
  year =         "2009",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 2 18:46:49 MST 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Sagot:2009:EE,
  author =       "Marie-France Sagot",
  title =        "{EIC} Editorial",
  journal =      j-TCBB,
  volume =       "6",
  number =       "2",
  pages =        "177--177",
  month =        apr,
  year =         "2009",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.44",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 1 17:03:22 MDT 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Mandoiu:2009:GEI,
  author =       "Ion Mandoiu and Yi Pan and Raj Sunderraman and
                 Alexander Zelikovsky",
  title =        "{Guest Editors}' Introduction to the Special Section
                 on Bioinformatics Research and Applications",
  journal =      j-TCBB,
  volume =       "6",
  number =       "2",
  pages =        "178--179",
  month =        apr,
  year =         "2009",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.45",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 1 17:03:22 MDT 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Treangen:2009:NHL,
  author =       "Todd J. Treangen and Aaron E. Darling and Guillaume
                 Achaz and Mark A. Ragan and Xavier Messeguer and
                 Eduardo P. C. Rocha",
  title =        "A Novel Heuristic for Local Multiple Alignment of
                 Interspersed {DNA} Repeats",
  journal =      j-TCBB,
  volume =       "6",
  number =       "2",
  pages =        "180--189",
  month =        apr,
  year =         "2009",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.9",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 1 17:03:22 MDT 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Pairwise local sequence alignment methods have been
                 the prevailing technique to identify homologous
                 nucleotides between related species. However, existing
                 methods that identify and align all homologous
                 nucleotides in one or more genomes have suffered from
                 poor scalability and limited accuracy. We propose a
                 novel method that couples a gapped extension heuristic
                 with an efficient filtration method for identifying
                 interspersed repeats in genome sequences. During gapped
                 extension, we use the MUSCLE implementation of
                 progressive global multiple alignment with iterative
                 refinement. The resulting gapped extensions potentially
                 contain alignments of unrelated sequence. We detect and
                 remove such undesirable alignments using a hidden
                 Markov model (HMM) to predict the posterior probability
                 of homology. The HMM emission frequencies for
                 nucleotide substitutions can be derived from any
                 time-reversible nucleotide substitution matrix. We
                 evaluate the performance of our method and previous
                 approaches on a hybrid data set of real genomic DNA
                 with simulated interspersed repeats. Our method
                 outperforms a related method in terms of sensitivity,
                 positive predictive value, and localizing boundaries of
                 homology. The described methods have been implemented
                 in freely available software, Repeatoire, available
                 from:
                 \path=http://wwwabi.snv.jussieu.fr/public/Repeatoire=.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "DNA repeats; gapped extension.; genome comparison;
                 hidden Markov model; local multiple alignment; Sequence
                 alignment",
}

@Article{Qiu:2009:FMK,
  author =       "Shibin Qiu and Terran Lane",
  title =        "A Framework for Multiple Kernel Support Vector
                 Regression and Its Applications to {siRNA} Efficacy
                 Prediction",
  journal =      j-TCBB,
  volume =       "6",
  number =       "2",
  pages =        "190--199",
  month =        apr,
  year =         "2009",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.139",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 1 17:03:22 MDT 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The cell defense mechanism of RNA interference has
                 applications in gene function analysis and promising
                 potentials in human disease therapy. To effectively
                 silence a target gene, it is desirable to select
                 appropriate initiator siRNA molecules having
                 satisfactory silencing capabilities. Computational
                 prediction for silencing efficacy of siRNAs can assist
                 this screening process before using them in biological
                 experiments. String kernel functions, which operate
                 directly on the string objects representing siRNAs and
                 target mRNAs, have been applied to support vector
                 regression for the prediction and improved accuracy
                 over numerical kernels in multidimensional vector
                 spaces constructed from descriptors of siRNA design
                 rules. To fully utilize information provided by string
                 and numerical data, we propose to unify the two in a
                 kernel feature space by devising a multiple kernel
                 regression framework where a linear combination of the
                 kernels is used. We formulate the multiple kernel
                 learning into a quadratically constrained quadratic
                 programming (QCQP) problem, which although yields
                 global optimal solution, is computationally demanding
                 and requires a commercial solver package. We further
                 propose three heuristics based on the principle of
                 kernel-target alignment and predictive accuracy.
                 Empirical results demonstrate that multiple kernel
                 regression can improve accuracy, decrease model
                 complexity by reducing the number of support vectors,
                 and speed up computational performance dramatically. In
                 addition, multiple kernel regression evaluates the
                 importance of constituent kernels, which for the siRNA
                 efficacy prediction problem, compares the relative
                 significance of the design rules. Finally, we give
                 insights into the multiple kernel regression mechanism
                 and point out possible extensions.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "multiple kernel heuristics; Multiple kernel learning;
                 QCQP optimization; RNA interference; siRNA efficacy.;
                 support vector regression",
}

@Article{Park:2009:NBI,
  author =       "Yongjin Park and Stanley Shackney and Russell
                 Schwartz",
  title =        "Network-Based Inference of Cancer Progression from
                 Microarray Data",
  journal =      j-TCBB,
  volume =       "6",
  number =       "2",
  pages =        "200--212",
  month =        apr,
  year =         "2009",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.126",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 1 17:03:22 MDT 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Cancer cells exhibit a common phenotype of
                 uncontrolled cell growth, but this phenotype may arise
                 from many different combinations of mutations. By
                 inferring how cells evolve in individual tumors, a
                 process called cancer progression, we may be able to
                 identify important mutational events for different
                 tumor types, potentially leading to new therapeutics
                 and diagnostics. Prior work has shown that it is
                 possible to infer frequent progression pathways by
                 using gene expression profiles to estimate
                 ``distances'' between tumors. Here, we apply gene
                 network models to improve these estimates of
                 evolutionary distance by controlling for correlations
                 among coregulated genes. We test three variants of this
                 approach: one using an optimized best-fit network,
                 another using sampling to infer a high-confidence
                 subnetwork, and one using a modular network inferred
                 from clusters of similarly expressed genes. Application
                 to lung cancer and breast cancer microarray data sets
                 shows small improvements in phylogenies when correcting
                 from the optimized network and more substantial
                 improvements when correcting from the sampled or
                 modular networks. Our results suggest that a network
                 correction approach improves estimates of tumor
                 similarity, but sophisticated network models are needed
                 to control for the large hypothesis space and sparse
                 data currently available.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "Biology and genetics; graphs and networks; machine
                 learning.; trees",
}

@Article{Zhu:2009:GGA,
  author =       "Qian Zhu and Zaky Adam and Vicky Choi and David
                 Sankoff",
  title =        "Generalized Gene Adjacencies, Graph Bandwidth, and
                 Clusters in Yeast Evolution",
  journal =      j-TCBB,
  volume =       "6",
  number =       "2",
  pages =        "213--220",
  month =        apr,
  year =         "2009",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.121",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 1 17:03:22 MDT 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "We present a parameterized definition of gene clusters
                 that allows us to control the emphasis placed on
                 conserved order within a cluster. Though motivated by
                 biological rather than mathematical considerations,
                 this parameter turns out to be closely related to the
                 bandwidth parameter of a graph. Our focus will be on
                 how this parameter affects the characteristics of
                 clusters: how numerous they are, how large they are,
                 how rearranged they are, and to what extent they are
                 preserved from ancestor to descendant in a phylogenetic
                 tree. We infer the latter property by dynamic
                 programming optimization of the presence of individual
                 edges at the ancestral nodes of the phylogeny. We apply
                 our analysis to a set of genomes drawn from the Yeast
                 Gene Order Browser.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "Ashbya gossypii; Candida glabrata; Comparative
                 genomics; dynamic programming; evolution; gene
                 clusters; genome rearrangements; graph bandwidth;
                 Kluyveromyces lactis.; Kluyveromyces waltii; phylogeny;
                 Saccharomyces cerevisiae; yeast",
}

@Article{Bansal:2009:GDP,
  author =       "Mukul S. Bansal and Oliver Eulenstein and Andr{\'e}
                 Wehe",
  title =        "The Gene-Duplication Problem: Near-Linear Time
                 Algorithms for {NNI}-Based Local Searches",
  journal =      j-TCBB,
  volume =       "6",
  number =       "2",
  pages =        "221--231",
  month =        apr,
  year =         "2009",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.7",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 1 17:03:22 MDT 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The gene-duplication problem is to infer a species
                 supertree from a collection of gene trees that are
                 confounded by complex histories of gene-duplication
                 events. This problem is NP-complete and thus requires
                 efficient and effective heuristics. Existing heuristics
                 perform a stepwise search of the tree space, where each
                 step is guided by an exact solution to an instance of a
                 local search problem. A classical local search problem
                 is the {\tt NNI} search problem, which is based on the
                 nearest neighbor interchange operation. In this work,
                 we (1) provide a novel near-linear time algorithm for
                 the {\tt NNI} search problem, (2) introduce extensions
                 that significantly enlarge the search space of the {\tt
                 NNI} search problem, and (3) present algorithms for
                 these extended versions that are asymptotically just as
                 efficient as our algorithm for the {\tt NNI} search
                 problem. The exceptional speedup achieved in the
                 extended {\tt NNI} search problems makes the
                 gene-duplication problem more tractable for large-scale
                 phylogenetic analyses. We verify the performance of our
                 algorithms in a comparison study using sets of large
                 randomly generated gene trees.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "Computational phylogenetics; gene-duplication; local
                 search; supertrees; {\tt NNI}.",
}

@Article{Sun:2009:DPP,
  author =       "Yanni Sun and Jeremy Buhler",
  title =        "Designing Patterns and Profiles for Faster {HMM}
                 Search",
  journal =      j-TCBB,
  volume =       "6",
  number =       "2",
  pages =        "232--243",
  month =        apr,
  year =         "2009",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.14",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 1 17:03:22 MDT 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Profile HMMs are powerful tools for modeling conserved
                 motifs in proteins. They are widely used by search
                 tools to classify new protein sequences into families
                 based on domain architecture. However, the
                 proliferation of known motifs and new proteomic
                 sequence data poses a computational challenge for
                 search, requiring days of CPU time to annotate an
                 organism's proteome. It is highly desirable to speed up
                 HMM search in large databases. We design PROSITE-like
                 patterns and short profiles that are used as filters to
                 rapidly eliminate protein-motif pairs for which a full
                 profile HMM comparison does not yield a significant
                 match. The design of the pattern-based filters is
                 formulated as a multichoice knapsack problem.
                 Profile-based filters with high sensitivity are
                 extracted from a profile HMM based on their theoretical
                 sensitivity and false positive rate. Experiments show
                 that our profile-based filters achieve high sensitivity
                 (near 100 percent) while keeping around $ 20 \times $
                 speedup with respect to the unfiltered search program.
                 Pattern-based filters typically retain at least 90
                 percent of the sensitivity of the source HMM with $ 30
                 $--$ 40 \times $ speedup. The profile-based filters
                 have sensitivity comparable to the multistage filtering
                 strategy HMMERHEAD [15] and are faster in most of our
                 experiments.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "bioinformatics databases; Biology and genetics; hidden
                 Markov models.; sequence similarity search",
}

@Article{Shaik:2009:FAS,
  author =       "Jahangheer Shaik and Mohammed Yeasin",
  title =        "Fuzzy-Adaptive-Subspace-Iteration-Based Two-Way
                 Clustering of Microarray Data",
  journal =      j-TCBB,
  volume =       "6",
  number =       "2",
  pages =        "244--259",
  month =        apr,
  year =         "2009",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.15",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 1 17:03:22 MDT 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "This paper presents
                 Fuzzy-Adaptive-Subspace-Iteration-based Two-way
                 Clustering (FASIC) of microarray data for finding
                 differentially expressed genes (DEGs) from two-sample
                 microarray experiments. The concept of fuzzy membership
                 is introduced to transform the hard adaptive subspace
                 iteration (ASI) algorithm into a fuzzy-ASI algorithm to
                 perform two-way clustering. The proposed approach
                 follows a progressive framework to assign a relevance
                 value to genes associated with each cluster.
                 Subsequently, each gene cluster is scored and ranked
                 based on its potential to provide a correct
                 classification of the sample classes. These ranks are
                 converted into $P$ values using the $R$-test, and the
                 significance of each gene is determined. A fivefold
                 validation is performed on the DEGs selected using the
                 proposed approach. Empirical analyses on a number of
                 simulated microarray data sets are conducted to
                 quantify the results obtained using the proposed
                 approach. To exemplify the efficacy of the proposed
                 approach, further analyses on different real microarray
                 data sets are also performed.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "classification and association rules; Clustering; data
                 and knowledge visualization; data mining; feature
                 extraction or construction.",
}

@Article{Vignes:2009:GCI,
  author =       "Matthieu Vignes and Florence Forbes",
  title =        "Gene Clustering via Integrated {Markov} Models
                 Combining Individual and Pairwise Features",
  journal =      j-TCBB,
  volume =       "6",
  number =       "2",
  pages =        "260--270",
  month =        apr,
  year =         "2009",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2007.70248",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 1 17:03:22 MDT 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Clustering of genes into groups sharing common
                 characteristics is a useful exploratory technique for a
                 number of subsequent computational analysis. A wide
                 range of clustering algorithms have been proposed in
                 particular to analyze gene expression data, but most of
                 them consider genes as independent entities or include
                 relevant information on gene interactions in a
                 suboptimal way. We propose a probabilistic model that
                 has the advantage to account for individual data (e.g.,
                 expression) and pairwise data (e.g., interaction
                 information coming from biological networks)
                 simultaneously. Our model is based on hidden Markov
                 random field models in which parametric probability
                 distributions account for the distribution of
                 individual data. Data on pairs, possibly reflecting
                 distance or similarity measures between genes, are then
                 included through a graph, where the nodes represent the
                 genes, and the edges are weighted according to the
                 available interaction information. As a probabilistic
                 model, this model has many interesting theoretical
                 features. In addition, preliminary experiments on
                 simulated and real data show promising results and
                 points out the gain in using such an approach.
                 Availability: The software used in this work is written
                 in C++ and is available with other supplementary
                 material at
                 \path=http://mistis.inrialpes.fr/people/forbes/transparentia/supplementary.html=.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "gene expression.; Markov random fields; metabolic
                 networks; model-based clustering",
}

@Article{Heath:2009:SMN,
  author =       "Lenwood S. Heath and Allan A. Sioson",
  title =        "Semantics of Multimodal Network Models",
  journal =      j-TCBB,
  volume =       "6",
  number =       "2",
  pages =        "271--280",
  month =        apr,
  year =         "2009",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2007.70242",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 1 17:03:22 MDT 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "A multimodal network (MMN) is a novel graph-theoretic
                 formalism designed to capture the structure of
                 biological networks and to represent relationships
                 derived from multiple biological databases. MMNs
                 generalize the standard notions of graphs and
                 hypergraphs, which are the bases of current
                 diagrammatic representations of biological phenomena,
                 and incorporate the concept of mode. Each vertex of an
                 MMN is a biological entity, a biot, while each modal
                 hyperedge is a typed relationship, where the type is
                 given by the mode of the hyperedge. The semantics of
                 each modal hyperedge $e$ is given through denotational
                 semantics, where a valuation function $ f \_ {e} $
                 defines the relationship among the values of the
                 vertices incident on $e$. The meaning of an MMN is
                 denoted in terms of the semantics of a hyperedge
                 sequence. A companion paper defines MMNs and
                 concentrates on the structural aspects of MMNs. This
                 paper develops MMN denotational semantics when used as
                 a representation of the semantics of biological
                 networks and discusses applications of MMNs in managing
                 complex biological data.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "biological model; biological networks; biot;
                 denotational semantics.; graph; hypergraph; mode;
                 Multimodal network",
}

@Article{Arribas-Gil:2009:SAS,
  author =       "Ana Arribas-Gil and Dirk Metzler and Jean-Louis
                 Plouhinec",
  title =        "Statistical Alignment with a Sequence Evolution Model
                 Allowing Rate Heterogeneity along the Sequence",
  journal =      j-TCBB,
  volume =       "6",
  number =       "2",
  pages =        "281--295",
  month =        apr,
  year =         "2009",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2007.70246",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 1 17:03:22 MDT 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "We present a stochastic sequence evolution model to
                 obtain alignments and estimate mutation rates between
                 two homologous sequences. The model allows two possible
                 evolutionary behaviors along a DNA sequence in order to
                 determine conserved regions and take its heterogeneity
                 into account. In our model, the sequence is divided
                 into slow and fast evolution regions. The boundaries
                 between these sections are not known. It is our aim to
                 detect them. The evolution model is based on a fragment
                 insertion and deletion process working on fast regions
                 only and on a substitution process working on fast and
                 slow regions with different rates. This model induces a
                 pair hidden Markov structure at the level of
                 alignments, thus making efficient statistical alignment
                 algorithms possible. We propose two complementary
                 estimation methods, namely, a Gibbs sampler for
                 Bayesian estimation and a stochastic version of the EM
                 algorithm for maximum likelihood estimation. Both
                 algorithms involve the sampling of alignments. We
                 propose a partial alignment sampler, which is
                 computationally less expensive than the typical whole
                 alignment sampler. We show the convergence of the two
                 estimation algorithms when used with this partial
                 sampler. Our algorithms provide consistent estimates
                 for the mutation rates and plausible alignments and
                 sequence segmentations on both simulated and real
                 data.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "biology and genetics.; Markov processes; mathematics
                 and statistics; probabilistic algorithms; sequence
                 evolution",
}

@Article{Weber:2009:VET,
  author =       "Gunther H. Weber and Oliver Rubel and Min-Yu Huang and
                 Angela H. DePace and Charless C. Fowlkes and Soile V.
                 E. Keranen and Cris L. Luengo Hendriks and Hans Hagen
                 and David W. Knowles and Jitendra Malik and Mark D.
                 Biggin and Bernd Hamann",
  title =        "Visual Exploration of Three-Dimensional Gene
                 Expression Using Physical Views and Linked Abstract
                 Views",
  journal =      j-TCBB,
  volume =       "6",
  number =       "2",
  pages =        "296--309",
  month =        apr,
  year =         "2009",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2007.70249",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 1 17:03:22 MDT 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "During animal development, complex patterns of gene
                 expression provide positional information within the
                 embryo. To better understand the underlying gene
                 regulatory networks, the Berkeley Drosophila
                 Transcription Network Project (BDTNP) has developed
                 methods that support quantitative computational
                 analysis of three-dimensional (3D) gene expression in
                 early Drosophila embryos at cellular resolution. We
                 introduce PointCloudXplore (PCX), an interactive
                 visualization tool that supports visual exploration of
                 relationships between different genes' expression using
                 a combination of established visualization techniques.
                 Two aspects of gene expression are of particular
                 interest: (1) gene expression patterns defined by the
                 spatial locations of cells expressing a gene and (2)
                 relationships between the expression levels of multiple
                 genes. PCX provides users with two corresponding
                 classes of data views: (1) Physical Views based on the
                 spatial relationships of cells in the embryo and (2)
                 Abstract Views that discard spatial information and
                 plot expression levels of multiple genes with respect
                 to each other. Cell Selectors highlight data associated
                 with subsets of embryo cells within a View. Using
                 linking, these selected cells can be viewed in multiple
                 representations. We describe PCX as a 3D gene
                 expression visualization tool and provide examples of
                 how it has been used by BDTNP biologists to generate
                 new hypotheses.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "brushing; information visualization; Interactive data
                 exploration; multiple linked views; physical views;
                 scatter plots.; spatial expression patterns;
                 three-dimensional gene expression; visualization",
}

@Article{Dougherty:2009:CBM,
  author =       "Edward R. Dougherty and Marcel Brun and Jeffrey M.
                 Trent and Michael L. Bittner",
  title =        "Conditioning-Based Modeling of Contextual Genomic
                 Regulation",
  journal =      j-TCBB,
  volume =       "6",
  number =       "2",
  pages =        "310--320",
  month =        apr,
  year =         "2009",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2007.70247",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 1 17:03:22 MDT 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "A more complete understanding of the alterations in
                 cellular regulatory and control mechanisms that occur
                 in the various forms of cancer has been one of the
                 central targets of the genomic and proteomic methods
                 that allow surveys of the abundance and/or state of
                 cellular macromolecules. This preference is driven both
                 by the intractability of cancer to generic therapies,
                 assumed to be due to the highly varied molecular
                 etiologies observed in cancer, and by the opportunity
                 to discern and dissect the regulatory and control
                 interactions presented by the highly diverse assortment
                 of perturbations of regulation and control that arise
                 in cancer. Exploiting the opportunities for inference
                 on the regulatory and control connections offered by
                 these revealing system perturbations is fraught with
                 the practical problems that arise from the way
                 biological systems operate. Two classes of regulatory
                 action in biological systems are particularly inimical
                 to inference, convergent regulation, where a variety of
                 regulatory actions result in a common set of control
                 responses (crosstalk), and divergent regulation, where
                 a single regulatory action produces entirely different
                 sets of control responses, depending on cellular
                 context (conditioning). We have constructed a coarse
                 mathematical model of the propagation of regulatory
                 influence in such distributed, context-sensitive
                 regulatory networks that allows a quantitative
                 estimation of the amount of crosstalk and conditioning
                 associated with a candidate regulatory gene taken from
                 a set of genes that have been profiled over a series of
                 samples where the candidate's activity varies.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "Microarray; regulatory networks.",
}

@Article{Heath:2009:MNS,
  author =       "Lenwood S. Heath and Allan A. Sioson",
  title =        "Multimodal Networks: Structure and Operations",
  journal =      j-TCBB,
  volume =       "6",
  number =       "2",
  pages =        "321--332",
  month =        apr,
  year =         "2009",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2007.70243",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 1 17:03:22 MDT 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "A multimodal network (MMN) is a novel graph-theoretic
                 formalism designed to capture the structure of
                 biological networks and to represent relationships
                 derived from multiple biological databases. MMNs
                 generalize the standard notions of graphs and
                 hypergraphs, which are the bases of current
                 diagrammatic representations of biological phenomena
                 and incorporate the concept of mode. Each vertex of an
                 MMN is a biological entity, a biot, while each modal
                 hyperedge is a typed relationship, where the type is
                 given by the mode of the hyperedge. The current paper
                 defines MMNs and concentrates on the structural aspects
                 of MMNs. A companion paper develops MMNs as a
                 representation of the semantics of biological networks
                 and discusses applications of the MMNs in managing
                 complex biological data. The MMN model has been
                 implemented in a database system containing multiple
                 kinds of biological networks.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "biological networks; biot.; graph; hypergraph; mode;
                 Multimodal network",
}

@Article{Yukinawa:2009:OAB,
  author =       "Naoto Yukinawa and Shigeyuki Oba and Kikuya Kato and
                 Shin Ishii",
  title =        "Optimal Aggregation of Binary Classifiers for
                 Multiclass Cancer Diagnosis Using Gene Expression
                 Profiles",
  journal =      j-TCBB,
  volume =       "6",
  number =       "2",
  pages =        "333--343",
  month =        apr,
  year =         "2009",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2007.70239",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 1 17:03:22 MDT 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Multiclass classification is one of the fundamental
                 tasks in bioinformatics and typically arises in cancer
                 diagnosis studies by gene expression profiling. There
                 have been many studies of aggregating binary
                 classifiers to construct a multiclass classifier based
                 on one-versus-the-rest (1R), one-versus-one (11), or
                 other coding strategies, as well as some comparison
                 studies between them. However, the studies found that
                 the best coding depends on each situation. Therefore, a
                 new problem, which we call the ``optimal coding
                 problem,'' has arisen: how can we determine which
                 coding is the optimal one in each situation? To
                 approach this optimal coding problem, we propose a
                 novel framework for constructing a multiclass
                 classifier, in which each binary classifier to be
                 aggregated has a weight value to be optimally tuned
                 based on the observed data. Although there is no a
                 priori answer to the optimal coding problem, our weight
                 tuning method can be a consistent answer to the
                 problem. We apply this method to various classification
                 problems including a synthesized data set and some
                 cancer diagnosis data sets from gene expression
                 profiling. The results demonstrate that, in most
                 situations, our method can improve classification
                 accuracy over simple voting heuristics and is better
                 than or comparable to state-of-the-art multiclass
                 predictors.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "cancer diagnosis.; error correcting output coding;
                 gene expression profiling; Multiclass classification",
}

@Article{Olman:2009:PCA,
  author =       "Victor Olman and Fenglou Mao and Hongwei Wu and Ying
                 Xu",
  title =        "Parallel Clustering Algorithm for Large Data Sets with
                 Applications in Bioinformatics",
  journal =      j-TCBB,
  volume =       "6",
  number =       "2",
  pages =        "344--352",
  month =        apr,
  year =         "2009",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2007.70272",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 1 17:03:22 MDT 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Large sets of bioinformatical data provide a challenge
                 in time consumption while solving the cluster
                 identification problem, and that is why a parallel
                 algorithm is so needed for identifying dense clusters
                 in a noisy background. Our algorithm works on a graph
                 representation of the data set to be analyzed. It
                 identifies clusters through the identification of
                 densely intraconnected subgraphs. We have employed a
                 minimum spanning tree (MST) representation of the graph
                 and solve the cluster identification problem using this
                 representation. The computational bottleneck of our
                 algorithm is the construction of an MST of a graph, for
                 which a parallel algorithm is employed. Our high-level
                 strategy for the parallel MST construction algorithm is
                 to first partition the graph, then construct MSTs for
                 the partitioned subgraphs and auxiliary bipartite
                 graphs based on the subgraphs, and finally merge these
                 MSTs to derive an MST of the original graph. The
                 computational results indicate that when running on 150
                 CPUs, our algorithm can solve a cluster identification
                 problem on a data set with 1,000,000 data points almost
                 100 times faster than on single CPU, indicating that
                 this program is capable of handling very large data
                 clustering problems in an efficient manner. We have
                 implemented the clustering algorithm as the software
                 CLUMP.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "clustering algorithm; genome application; parallel
                 processing.; Pattern recognition",
}

@Article{Paul:2009:PCC,
  author =       "Topon Kumar Paul and Hitoshi Iba",
  title =        "Prediction of Cancer Class with Majority Voting
                 Genetic Programming Classifier Using Gene Expression
                 Data",
  journal =      j-TCBB,
  volume =       "6",
  number =       "2",
  pages =        "353--367",
  month =        apr,
  year =         "2009",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2007.70245",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 1 17:03:22 MDT 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "In order to get a better understanding of different
                 types of cancers and to find the possible biomarkers
                 for diseases, recently, many researchers are analyzing
                 the gene expression data using various machine learning
                 techniques. However, due to a very small number of
                 training samples compared to the huge number of genes
                 and class imbalance, most of these methods suffer from
                 overfitting. In this paper, we present a majority
                 voting genetic programming classifier (MVGPC) for the
                 classification of microarray data. Instead of a single
                 rule or a single set of rules, we evolve multiple rules
                 with genetic programming (GP) and then apply those
                 rules to test samples to determine their labels with
                 majority voting technique. By performing experiments on
                 four different public cancer data sets, including
                 multiclass data sets, we have found that the test
                 accuracies of MVGPC are better than those of other
                 methods, including AdaBoost with GP. Moreover, some of
                 the more frequently occurring genes in the
                 classification rules are known to be associated with
                 the types of cancers being studied in this paper.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "Classifier design and evaluation; data mining;
                 evolutionary computing and genetic algorithm; feature
                 extraction; gene expression; majority voting.",
}

@Article{Sagot:2009:EEI,
  author =       "Marie-France Sagot",
  title =        "{EIC} Editorial: Introducing New {Associate Editors}",
  journal =      j-TCBB,
  volume =       "6",
  number =       "3",
  pages =        "369--369",
  month =        jul,
  year =         "2009",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.66",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Tue Aug 11 18:13:22 MDT 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Bi:2009:MCE,
  author =       "Chengpeng Bi",
  title =        "A {Monte Carlo} {EM} Algorithm for {De Novo Motif}
                 Discovery in Biomolecular Sequences",
  journal =      j-TCBB,
  volume =       "6",
  number =       "3",
  pages =        "370--386",
  month =        jul,
  year =         "2009",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.103",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Tue Aug 11 18:13:22 MDT 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Motif discovery methods play pivotal roles in
                 deciphering the genetic regulatory codes (i.e., motifs)
                 in genomes as well as in locating conserved domains in
                 protein sequences. The Expectation Maximization (EM)
                 algorithm is one of the most popular methods used in de
                 novo motif discovery. Based on the position weight
                 matrix (PWM) updating technique, this paper presents a
                 Monte Carlo version of the EM motif-finding algorithm
                 that carries out stochastic sampling in local alignment
                 space to overcome the conventional EM's main drawback
                 of being trapped in a local optimum. The newly
                 implemented algorithm is named as Monte Carlo EM Motif
                 Discovery Algorithm (MCEMDA). MCEMDA starts from an
                 initial model, and then it iteratively performs Monte
                 Carlo simulation and parameter update until
                 convergence. A log-likelihood profiling technique
                 together with the top-$k$ strategy is introduced to
                 cope with the phase shifts and multiple modal issues in
                 motif discovery problem. A novel grouping motif
                 alignment (GMA) algorithm is designed to select motifs
                 by clustering a population of candidate local
                 alignments and successfully applied to subtle motif
                 discovery. MCEMDA compares favorably to other popular
                 PWM-based and word enumerative motif algorithms tested
                 using simulated $ (l, d) $-motif cases, documented
                 prokaryotic, and eukaryotic DNA motif sequences.
                 Finally, MCEMDA is applied to detect large blocks of
                 conserved domains using protein benchmarks and exhibits
                 its excellent capacity while compared with other
                 multiple sequence alignment methods.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "Expectation maximization (EM); Monte Carlo EM; motif
                 discovery; multiple sequence alignment; transcriptional
                 regulation.",
}

@Article{Stoye:2009:UAR,
  author =       "Jens Stoye and Roland Wittler",
  title =        "A Unified Approach for Reconstructing Ancient Gene
                 Clusters",
  journal =      j-TCBB,
  volume =       "6",
  number =       "3",
  pages =        "387--400",
  month =        jul,
  year =         "2009",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.135",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Tue Aug 11 18:13:22 MDT 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The order of genes in genomes provides extensive
                 information. In comparative genomics, differences or
                 similarities of gene orders are determined to predict
                 functional relations of genes or phylogenetic relations
                 of genomes. For this purpose, various combinatorial
                 models can be used to identify gene clusters --- groups
                 of genes that are colocated in a set of genomes. We
                 introduce a unified approach to model gene clusters and
                 define the problem of labeling the inner nodes of a
                 given phylogenetic tree with sets of gene clusters. Our
                 optimization criterion in this context combines two
                 properties: parsimony, i.e., the number of gains and
                 losses of gene clusters has to be minimal, and
                 consistency, i.e., for each ancestral node, there must
                 exist at least one potential gene order that contains
                 all the reconstructed clusters. We present and evaluate
                 an exact algorithm to solve this problem. Despite its
                 exponential worst-case time complexity, our method is
                 suitable even for large-scale data. We show the
                 effectiveness and efficiency on both simulated and real
                 data.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "Comparative genomics; consistency.; gene cluster; gene
                 cluster reconstruction; gene order; parsimony;
                 phylogeny",
}

@Article{Chen:2009:AAM,
  author =       "Xin Chen and Yun Cui",
  title =        "An Approximation Algorithm for the Minimum Breakpoint
                 Linearization Problem",
  journal =      j-TCBB,
  volume =       "6",
  number =       "3",
  pages =        "401--409",
  month =        jul,
  year =         "2009",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.3",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Tue Aug 11 18:13:22 MDT 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "In the recent years, there has been a growing interest
                 in inferring the total order of genes or markers on a
                 chromosome, since current genetic mapping efforts might
                 only suffice to produce a partial order. Many
                 interesting optimization problems were thus formulated
                 in the framework of genome rearrangement. As an
                 important one among them, the minimum breakpoint
                 linearization (MBL) problem is to find the total order
                 of a partially ordered genome that minimizes its
                 breakpoint distance to a reference genome whose genes
                 are already totally ordered. It was previously shown to
                 be NP-hard, and the algorithms proposed so far are all
                 heuristic. In this paper, we present an $ m^2 + m \over
                 2 $-approximation algorithm for the MBL problem, where
                 $m$ is the number of gene maps that are combined
                 together to form a partial order of the genome under
                 investigation.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "approximation algorithms.; breakpoint distance;
                 Comparative genomics; partially ordered genomes",
}

@Article{Wang:2009:EKF,
  author =       "Zidong Wang and Xiaohui Liu and Yurong Liu and Jinling
                 Liang and Veronica Vinciotti",
  title =        "An Extended {Kalman} Filtering Approach to Modeling
                 Nonlinear Dynamic Gene Regulatory Networks via Short
                 Gene Expression Time Series",
  journal =      j-TCBB,
  volume =       "6",
  number =       "3",
  pages =        "410--419",
  month =        jul,
  year =         "2009",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.5",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Tue Aug 11 18:13:22 MDT 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "In this paper, the extended Kalman filter (EKF)
                 algorithm is applied to model the gene regulatory
                 network from gene time series data. The gene regulatory
                 network is considered as a nonlinear dynamic stochastic
                 model that consists of the gene measurement equation
                 and the gene regulation equation. After specifying the
                 model structure, we apply the EKF algorithm for
                 identifying both the model parameters and the actual
                 value of gene expression levels. It is shown that the
                 EKF algorithm is an online estimation algorithm that
                 can identify a large number of parameters (including
                 parameters of nonlinear functions) through iterative
                 procedure by using a small number of observations. Four
                 real-world gene expression data sets are employed to
                 demonstrate the effectiveness of the EKF algorithm, and
                 the obtained models are evaluated from the viewpoint of
                 bioinformatics.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "clustering; DNA microarray technology; extended Kalman
                 filtering; gene expression; Modeling; time series
                 data.",
}

@Article{Bryant:2009:CDT,
  author =       "David Bryant and Mike Steel",
  title =        "Computing the Distribution of a Tree Metric",
  journal =      j-TCBB,
  volume =       "6",
  number =       "3",
  pages =        "420--426",
  month =        jul,
  year =         "2009",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.32",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Tue Aug 11 18:13:22 MDT 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The Robinson--Foulds (RF) distance is by far the most
                 widely used measure of dissimilarity between trees.
                 Although the distribution of these distances has been
                 investigated for 20 years, an algorithm that is
                 explicitly polynomial time has yet to be described for
                 computing the distribution for trees around a given
                 tree. In this paper, we derive a polynomial-time
                 algorithm for this distribution. We show how the
                 distribution can be approximated by a Poisson
                 distribution determined by the proportion of leaves
                 that lie in ``cherries'' of the given tree. We also
                 describe how our results can be used to derive
                 normalization constants that are required in a recently
                 proposed maximum likelihood approach to supertree
                 construction.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "Biology and genetics; discrete mathematics
                 applications; normalization constant.; phylogenetics;
                 Poisson approximation; Robinson--Foulds distance;
                 trees",
}

@Article{Hulsman:2009:EOK,
  author =       "Marc Hulsman and Marcel J. T. Reinders and Dick de
                 Ridder",
  title =        "Evolutionary Optimization of Kernel Weights Improves
                 Protein Complex Comembership Prediction",
  journal =      j-TCBB,
  volume =       "6",
  number =       "3",
  pages =        "427--437",
  month =        jul,
  year =         "2009",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.137",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Tue Aug 11 18:13:22 MDT 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "In recent years, more and more high-throughput data
                 sources useful for protein complex prediction have
                 become available (e.g., gene sequence, mRNA expression,
                 and interactions). The integration of these different
                 data sources can be challenging. Recently, it has been
                 recognized that kernel-based classifiers are well
                 suited for this task. However, the different kernels
                 (data sources) are often combined using equal weights.
                 Although several methods have been developed to
                 optimize kernel weights, no large-scale example of an
                 improvement in classifier performance has been shown
                 yet. In this work, we employ an evolutionary algorithm
                 to determine weights for a larger set of kernels by
                 optimizing a criterion based on the area under the ROC
                 curve. We show that setting the right kernel weights
                 can indeed improve performance. We compare this to the
                 existing kernel weight optimization methods (i.e.,
                 (regularized) optimization of the SVM criterion or
                 aligning the kernel with an ideal kernel) and find that
                 these do not result in a significant performance
                 improvement and can even cause a decrease in
                 performance. Results also show that an expert approach
                 of assigning high weights to features with high
                 individual performance is not necessarily the best
                 strategy.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "biology and genetics; Classifier design and
                 evaluation; evolutionary computing and genetic
                 algorithms.",
}

@Article{Chen:2009:IAA,
  author =       "Zhi-Zhong Chen and Lusheng Wang",
  title =        "Improved Approximation Algorithms for Reconstructing
                 the History of Tandem Repeats",
  journal =      j-TCBB,
  volume =       "6",
  number =       "3",
  pages =        "438--453",
  month =        jul,
  year =         "2009",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.122",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Tue Aug 11 18:13:22 MDT 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Some genetic diseases in human beings are dominated by
                 short sequences repeated consecutively called tandem
                 repeats. Once a region containing tandem repeats is
                 found, it is of great interest to study the history of
                 creating the repeats. The computational problem of
                 reconstructing the duplication history of tandem
                 repeats has been studied extensively in the literature.
                 Almost all previous studies focused on the simplest
                 case where the size of each duplication block is 1.
                 Only recently we succeeded in giving the first
                 polynomial-time approximation algorithm with a
                 guaranteed ratio for a more general case where the size
                 of each duplication block is at most $2$; the algorithm
                 achieves a ratio of $6$ and runs in $ O(n^{11}) $ time.
                 In this paper, we present two new polynomial-time
                 approximation algorithms for this more general case.
                 One of them achieves a ratio of $5$ and runs in $
                 O(n^9) $ time, while the other achieves a ratio of $
                 2.5 + \epsilon $ for any constant $ \epsilon > 0 $ but
                 runs slower.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "approximation algorithms.; Computational biology",
}

@Article{Cardona:2009:MPNb,
  author =       "Gabriel Cardona and Merce Llabres and Francesc
                 Rossello and Gabriel Valiente",
  title =        "Metrics for Phylogenetic Networks {II}: Nodal and
                 Triplets Metrics",
  journal =      j-TCBB,
  volume =       "6",
  number =       "3",
  pages =        "454--469",
  month =        jul,
  year =         "2009",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.127",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Tue Aug 11 18:13:22 MDT 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The assessment of phylogenetic network reconstruction
                 methods requires the ability to compare phylogenetic
                 networks. This is the second in a series of papers
                 devoted to the analysis and comparison of metrics for
                 tree-child time consistent phylogenetic networks on the
                 same set of taxa. In this paper, we generalize to
                 phylogenetic networks two metrics that have already
                 been introduced in the literature for phylogenetic
                 trees: the nodal distance and the triplets distance. We
                 prove that they are metrics on any class of tree-child
                 time consistent phylogenetic networks on the same set
                 of taxa, as well as some basic properties for them. To
                 prove these results, we introduce a reduction/expansion
                 procedure that can be used not only to establish
                 properties of tree-child time consistent phylogenetic
                 networks by induction, but also to generate all
                 tree-child time consistent phylogenetic networks with a
                 given number of leaves.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "nodal distance; partition distance; Phylogenetic
                 network; temporal representation; time consistency;
                 tree-child phylogenetic network; triplets distance.",
}

@Article{Sotiropoulos:2009:MRM,
  author =       "Vassilios Sotiropoulos and Marrie-Nathalie
                 Contou-Carrere and Prodromos Daoutidis and Yiannis N.
                 Kaznessis",
  title =        "Model Reduction of Multiscale Chemical {Langevin}
                 Equations: {A} Numerical Case Study",
  journal =      j-TCBB,
  volume =       "6",
  number =       "3",
  pages =        "470--482",
  month =        jul,
  year =         "2009",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.23",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Tue Aug 11 18:13:22 MDT 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Two very important characteristics of biological
                 reaction networks need to be considered carefully when
                 modeling these systems. First, models must account for
                 the inherent probabilistic nature of systems far from
                 the thermodynamic limit. Often, biological systems
                 cannot be modeled with traditional
                 continuous-deterministic models. Second, models must
                 take into consideration the disparate spectrum of time
                 scales observed in biological phenomena, such as slow
                 transcription events and fast dimerization reactions.
                 In the last decade, significant efforts have been
                 expended on the development of stochastic chemical
                 kinetics models to capture the dynamics of biomolecular
                 systems, and on the development of robust multiscale
                 algorithms, able to handle stiffness. In this paper,
                 the focus is on the dynamics of reaction sets governed
                 by stiff chemical Langevin equations, i.e., stiff
                 stochastic differential equations. These are
                 particularly challenging systems to model, requiring
                 prohibitively small integration step sizes. We describe
                 and illustrate the application of a semianalytical
                 reduction framework for chemical Langevin equations
                 that results in significant gains in computational
                 cost.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "chemical Langevin equations (CLEs); Model reduction;
                 multiscale models; stiff biomolecular systems.;
                 stochastic chemical kinetics",
}

@Article{Roytberg:2009:SSP,
  author =       "Mikhail Roytberg and Anna Gambin and Laurent Noe and
                 Slawomir Lasota and Eugenia Furletova and Ewa Szczurek
                 and Gregory Kucherov",
  title =        "On Subset Seeds for Protein Alignment",
  journal =      j-TCBB,
  volume =       "6",
  number =       "3",
  pages =        "483--494",
  month =        jul,
  year =         "2009",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.4",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Tue Aug 11 18:13:22 MDT 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "We apply the concept of subset seeds proposed in [1]
                 to similarity search in protein sequences. The main
                 question studied is the design of efficient seed
                 alphabets to construct seeds with optimal
                 sensitivity/selectivity trade-offs. We propose several
                 different design methods and use them to construct
                 several alphabets. We then perform a comparative
                 analysis of seeds built over those alphabets and
                 compare them with the standard Blastp seeding method
                 [2], [3], as well as with the family of vector seeds
                 proposed in [4]. While the formalism of subset seeds is
                 less expressive (but less costly to implement) than the
                 cumulative principle used in Blastp and vector seeds,
                 our seeds show a similar or even better performance
                 than Blastp on Bernoulli models of proteins compatible
                 with the common BLOSUM62 matrix. Finally, we perform a
                 large-scale benchmarking of our seeds against several
                 main databases of protein alignments. Here again, the
                 results show a comparable or better performance of our
                 seeds versus Blastp.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "local alignment; multiple seeds; protein databases;
                 Protein sequences; seed alphabet; seeds; selectivity.;
                 sensitivity; similarity search; subset seeds",
}

@Article{Jin:2009:PSP,
  author =       "Guohua Jin and Luay Nakhleh and Sagi Snir and Tamir
                 Tuller",
  title =        "Parsimony Score of Phylogenetic Networks: Hardness
                 Results and a Linear-Time Heuristic",
  journal =      j-TCBB,
  volume =       "6",
  number =       "3",
  pages =        "495--505",
  month =        jul,
  year =         "2009",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.119",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Tue Aug 11 18:13:22 MDT 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Phylogenies --- the evolutionary histories of groups
                 of organisms --- play a major role in representing the
                 interrelationships among biological entities. Many
                 methods for reconstructing and studying such
                 phylogenies have been proposed, almost all of which
                 assume that the underlying history of a given set of
                 species can be represented by a binary tree. Although
                 many biological processes can be effectively modeled
                 and summarized in this fashion, others cannot:
                 recombination, hybrid speciation, and horizontal gene
                 transfer result in networks of relationships rather
                 than trees of relationships. In previous works, we
                 formulated a maximum parsimony (MP) criterion for
                 reconstructing and evaluating phylogenetic networks,
                 and demonstrated its quality on biological as well as
                 synthetic data sets. In this paper, we provide further
                 theoretical results as well as a very fast heuristic
                 algorithm for the MP criterion of phylogenetic
                 networks. In particular, we provide a novel
                 combinatorial definition of phylogenetic networks in
                 terms of ``forbidden cycles,'' and provide detailed
                 hardness and hardness of approximation proofs for the
                 ``small'' MP problem. We demonstrate the performance of
                 our heuristic in terms of time and accuracy on both
                 biological and synthetic data sets. Finally, we explain
                 the difference between our model and a similar one
                 formulated by Nguyen et al., and describe the
                 implications of this difference on the hardness and
                 approximation results.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "hardness and approximation.; horizontal gene transfer;
                 Maximum parsimony; phylogenetic networks",
}

@Article{Thomas:2009:PDS,
  author =       "John Thomas and Naren Ramakrishnan and Chris
                 Bailey-Kellogg",
  title =        "Protein Design by Sampling an Undirected Graphical
                 Model of Residue Constraints",
  journal =      j-TCBB,
  volume =       "6",
  number =       "3",
  pages =        "506--516",
  month =        jul,
  year =         "2009",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.124",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Tue Aug 11 18:13:22 MDT 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "This paper develops an approach for designing protein
                 variants by sampling sequences that satisfy residue
                 constraints encoded in an undirected probabilistic
                 graphical model. Due to evolutionary pressures on
                 proteins to maintain structure and function, the
                 sequence record of a protein family contains valuable
                 information regarding position-specific residue
                 conservation and coupling (or covariation) constraints.
                 Representing these constraints with a graphical model
                 provides two key benefits for protein design: a
                 probabilistic semantics enabling evaluation of possible
                 sequences for consistency with the constraints, and an
                 explicit factorization of residue dependence and
                 independence supporting efficient exploration of the
                 constrained sequence space. We leverage these benefits
                 in developing two complementary MCMC algorithms for
                 protein design: constrained shuffling mixes wild-type
                 sequences positionwise and evaluates graphical model
                 likelihood, while component sampling directly generates
                 sequences by sampling clique values and propagating to
                 other cliques. We apply our methods to design WW
                 domains. We demonstrate that likelihood under a model
                 of wild-type WWs is highly predictive of foldedness of
                 new WWs. We then show both theoretical and rapid
                 empirical convergence of our algorithms in generating
                 high-likelihood, diverse new sequences. We further show
                 that these sequences capture the original sequence
                 constraints, yielding a model as predictive of
                 foldedness as the original one.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "graphical models; Markov chain Monte Carlo (MCMC).;
                 Protein design; residue coupling",
}

@Article{Smith:2009:RSD,
  author =       "Jennifer A. Smith",
  title =        "{RNA} Search with Decision Trees and Partial
                 Covariance Models",
  journal =      j-TCBB,
  volume =       "6",
  number =       "3",
  pages =        "517--527",
  month =        jul,
  year =         "2009",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.120",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Tue Aug 11 18:13:22 MDT 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The use of partial covariance models to search for RNA
                 family members in genomic sequence databases is
                 explored. The partial models are formed from contiguous
                 subranges of the overall RNA family multiple alignment
                 columns. A binary decision-tree framework is presented
                 for choosing the order to apply the partial models and
                 the score thresholds on which to make the decisions.
                 The decision trees are chosen to minimize computation
                 time subject to the constraint that all of the training
                 sequences are passed to the full covariance model for
                 final evaluation. Computational intelligence methods
                 are suggested to select the decision tree since the
                 tree can be quite complex and there is no obvious
                 method to build the tree in these cases. Experimental
                 results from seven RNA families shows execution times
                 of 0.066-0.268 relative to using the full covariance
                 model alone. Tests on the full sets of known sequences
                 for each family show that at least 95 percent of these
                 sequences are found for two families and 100 percent
                 for five others. Since the full covariance model is run
                 on all sequences accepted by the partial model decision
                 tree, the false alarm rate is at least as low as that
                 of the full model alone.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "Bioinformatics; computational intelligence; covariance
                 models; decision trees; RNA database search.",
}

@Article{Anonymous:2010:EN,
  author =       "Anonymous",
  title =        "Editor's Note",
  journal =      j-TCBB,
  volume =       "7",
  number =       "1",
  pages =        "1--1",
  month =        jan,
  year =         "2010",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 15 18:56:53 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Diaz:2010:ADL,
  author =       "Ester Diaz and Guillermo Ayala and Maria
                 Diaz-Fernandez and Liang Gong and Derek Toomre",
  title =        "Automatic Detection of Large Dense-Core Vesicles in
                 Secretory Cells and Statistical Analysis of Their
                 Intracellular Distribution",
  journal =      j-TCBB,
  volume =       "7",
  number =       "1",
  pages =        "2--11",
  month =        jan,
  year =         "2010",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 15 18:56:53 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Lushbough:2010:BSI,
  author =       "Carol Lushbough and Michael K. Bergman and Carolyn J.
                 Lawrence and Doug Jennewein and Volker Brendel",
  title =        "{BioExtract Server} --- an Integrated
                 Workflow-Enabling System to Access and Analyze
                 Heterogeneous, Distributed Biomolecular Data",
  journal =      j-TCBB,
  volume =       "7",
  number =       "1",
  pages =        "12--24",
  month =        jan,
  year =         "2010",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 15 18:56:53 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Zhu:2010:FSG,
  author =       "Shenghuo Zhu and Dingding Wang and Kai Yu and Tao Li
                 and Yihong Gong",
  title =        "Feature Selection for Gene Expression Using
                 Model-Based Entropy",
  journal =      j-TCBB,
  volume =       "7",
  number =       "1",
  pages =        "25--36",
  month =        jan,
  year =         "2010",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 15 18:56:53 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Pehkonen:2010:HBS,
  author =       "Petri Pehkonen and Garry Wong and Petri Toronen",
  title =        "Heuristic {Bayesian} Segmentation for Discovery of
                 Coexpressed Genes within Genomic Regions",
  journal =      j-TCBB,
  volume =       "7",
  number =       "1",
  pages =        "37--49",
  month =        jan,
  year =         "2010",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 15 18:56:53 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Kustra:2010:DFC,
  author =       "Rafal Kustra and Adam Zagdanski",
  title =        "Data-Fusion in Clustering Microarray Data: Balancing
                 Discovery and Interpretability",
  journal =      j-TCBB,
  volume =       "7",
  number =       "1",
  pages =        "50--63",
  month =        jan,
  year =         "2010",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 15 18:56:53 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Rubel:2010:IDC,
  author =       "Oliver Rubel and Gunther H. Weber and Min-Yu Huang and
                 E. Wes Bethel and Mark D. Biggin and Charless C.
                 Fowlkes and Cris L. Luengo Hendriks and Soile V. E.
                 Keranen and Michael B. Eisen and David W. Knowles and
                 Jitendra Malik and Hans Hagen and Bernd Hamann",
  title =        "Integrating Data Clustering and Visualization for the
                 Analysis of {$3$D} Gene Expression Data",
  journal =      j-TCBB,
  volume =       "7",
  number =       "1",
  pages =        "64--79",
  month =        jan,
  year =         "2010",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 15 18:56:53 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Han:2010:MPC,
  author =       "Ju Han and Hang Chang and Kumari Andarawewa and Paul
                 Yaswen and Mary Helen Barcellos-Hoff and Bahram
                 Parvin",
  title =        "Multidimensional Profiling of Cell Surface Proteins
                 and Nuclear Markers",
  journal =      j-TCBB,
  volume =       "7",
  number =       "1",
  pages =        "80--90",
  month =        jan,
  year =         "2010",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 15 18:56:53 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Done:2010:PNH,
  author =       "Bogdan Done and Purvesh Khatri and Arina Done and
                 Sorin Draghici",
  title =        "Predicting Novel Human Gene Ontology Annotations Using
                 Semantic Analysis",
  journal =      j-TCBB,
  volume =       "7",
  number =       "1",
  pages =        "91--99",
  month =        jan,
  year =         "2010",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 15 18:56:53 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Liu:2010:SSV,
  author =       "Zhenqiu Liu and Shili Lin and Ming Tan",
  title =        "Sparse Support Vector Machines with {$ L_p $} Penalty
                 for Biomarker Identification",
  journal =      j-TCBB,
  volume =       "7",
  number =       "1",
  pages =        "100--107",
  month =        jan,
  year =         "2010",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 15 18:56:53 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Leung:2010:MFM,
  author =       "Yukyee Leung and Yeungsam Hung",
  title =        "A Multiple-Filter-Multiple-Wrapper Approach to Gene
                 Selection and Microarray Data Classification",
  journal =      j-TCBB,
  volume =       "7",
  number =       "1",
  pages =        "108--117",
  month =        jan,
  year =         "2010",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 15 18:56:53 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Perkins:2010:TBS,
  author =       "Theodore J. Perkins and Michael T. Hallett",
  title =        "A Trade-Off between Sample Complexity and
                 Computational Complexity in Learning {Boolean} Networks
                 from Time-Series Data",
  journal =      j-TCBB,
  volume =       "7",
  number =       "1",
  pages =        "118--125",
  month =        jan,
  year =         "2010",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 15 18:56:53 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Pelikan:2010:EPL,
  author =       "Richard Pelikan and Milos Hauskrecht",
  title =        "Efficient Peak-Labeling Algorithms for Whole-Sample
                 Mass Spectrometry Proteomics",
  journal =      j-TCBB,
  volume =       "7",
  number =       "1",
  pages =        "126--137",
  month =        jan,
  year =         "2010",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 15 18:56:53 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Mahata:2010:ECH,
  author =       "Pritha Mahata",
  title =        "Exploratory Consensus of Hierarchical Clusterings for
                 Melanoma and Breast Cancer",
  journal =      j-TCBB,
  volume =       "7",
  number =       "1",
  pages =        "138--152",
  month =        jan,
  year =         "2010",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 15 18:56:53 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Madeira:2010:IRM,
  author =       "Sara C. Madeira and Miguel C. Teixeira and Isabel
                 Sa-Correia and Arlindo L. Oliveira",
  title =        "Identification of Regulatory Modules in Time Series
                 Gene Expression Data Using a Linear Time Biclustering
                 Algorithm",
  journal =      j-TCBB,
  volume =       "7",
  number =       "1",
  pages =        "153--165",
  month =        jan,
  year =         "2010",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 15 18:56:53 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Mossel:2010:ILS,
  author =       "Elchanan Mossel and Sebastien Roch",
  title =        "Incomplete Lineage Sorting: Consistent Phylogeny
                 Estimation from Multiple Loci",
  journal =      j-TCBB,
  volume =       "7",
  number =       "1",
  pages =        "166--171",
  month =        jan,
  year =         "2010",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 15 18:56:53 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Freitas:2010:ICC,
  author =       "Alex A. Freitas and Daniela C. Wieser and Rolf
                 Apweiler",
  title =        "On the Importance of Comprehensible Classification
                 Models for Protein Function Prediction",
  journal =      j-TCBB,
  volume =       "7",
  number =       "1",
  pages =        "172--182",
  month =        jan,
  year =         "2010",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 15 18:56:53 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Alon:2010:AMP,
  author =       "Noga Alon and Benny Chor and Fabio Pardi and Anat
                 Rapoport",
  title =        "Approximate Maximum Parsimony and Ancestral Maximum
                 Likelihood",
  journal =      j-TCBB,
  volume =       "7",
  number =       "1",
  pages =        "183--187",
  month =        jan,
  year =         "2010",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 15 18:56:53 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Anonymous:2010:RL,
  author =       "Anonymous",
  title =        "2009 Reviewer's List",
  journal =      j-TCBB,
  volume =       "7",
  number =       "1",
  pages =        "188--190",
  month =        jan,
  year =         "2010",
  CODEN =        "ITCBCY",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Mar 15 18:56:53 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Sagot:2010:EE,
  author =       "Marie-France Sagot",
  title =        "{EIC} Editorial",
  journal =      j-TCBB,
  volume =       "7",
  number =       "2",
  pages =        "193--194",
  month =        apr,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.29",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri May 7 16:01:51 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Lonardi:2010:DMB,
  author =       "Stefano Lonardi and Jake Chen",
  title =        "Data Mining in Bioinformatics: Selected Papers from
                 {BIOKDD}",
  journal =      j-TCBB,
  volume =       "7",
  number =       "2",
  pages =        "195--196",
  month =        apr,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.28",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri May 7 16:01:51 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Smalter:2010:GGP,
  author =       "Aaron Smalter and Jun Huan and Yi Jia and Gerald
                 Lushington",
  title =        "{GPD}: {A} Graph Pattern Diffusion Kernel for Accurate
                 Graph Classification with Applications in
                 Cheminformatics",
  journal =      j-TCBB,
  volume =       "7",
  number =       "2",
  pages =        "197--207",
  month =        apr,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.80",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri May 7 16:01:51 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Graph data mining is an active research area. Graphs
                 are general modeling tools to organize information from
                 heterogeneous sources and have been applied in many
                 scientific, engineering, and business fields. With the
                 fast accumulation of graph data, building highly
                 accurate predictive models for graph data emerges as a
                 new challenge that has not been fully explored in the
                 data mining community. In this paper, we demonstrate a
                 novel technique called graph pattern diffusion (GPD)
                 kernel. Our idea is to leverage existing frequent
                 pattern discovery methods and to explore the
                 application of kernel classifier (e.g., support vector
                 machine) in building highly accurate graph
                 classification. In our method, we first identify all
                 frequent patterns from a graph database. We then map
                 subgraphs to graphs in the graph database and use a
                 process we call ``pattern diffusion'' to label nodes in
                 the graphs. Finally, we designed a graph alignment
                 algorithm to compute the inner product of two graphs.
                 We have tested our algorithm using a number of chemical
                 structure data. The experimental results demonstrate
                 that our method is significantly better than competing
                 methods such as those kernel functions based on paths,
                 cycles, and subgraphs.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "frequent subgraph mining.; graph alignment; Graph
                 classification",
}

@Article{Bogdanov:2010:MFP,
  author =       "Petko Bogdanov and Ambuj K. Singh",
  title =        "Molecular Function Prediction Using Neighborhood
                 Features",
  journal =      j-TCBB,
  volume =       "7",
  number =       "2",
  pages =        "208--217",
  month =        apr,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.81",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri May 7 16:01:51 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The recent advent of high-throughput methods has
                 generated large amounts of gene interaction data. This
                 has allowed the construction of genomewide networks. A
                 significant number of genes in such networks remain
                 uncharacterized and predicting the molecular function
                 of these genes remains a major challenge. A number of
                 existing techniques assume that genes with similar
                 functions are topologically close in the network. Our
                 hypothesis is that genes with similar functions observe
                 similar annotation patterns in their neighborhood,
                 regardless of the distance between them in the
                 interaction network. We thus predict molecular
                 functions of uncharacterized genes by comparing their
                 functional neighborhoods to genes of known function. We
                 propose a two-phase approach. First, we extract
                 functional neighborhood features of a gene using Random
                 Walks with Restarts. We then employ a KNN classifier to
                 predict the function of uncharacterized genes based on
                 the computed neighborhood features. We perform
                 leave-one-out validation experiments on two $S$.
                 cerevisiae interaction networks and show significant
                 improvements over previous techniques. Our technique
                 provides a natural control of the trade-off between
                 accuracy and coverage of prediction. We further propose
                 and evaluate prediction in sparse genomes by exploiting
                 features from well-annotated genomes.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "classification; feature extraction; functional
                 interaction network.; Gene function prediction",
}

@Article{Nakhleh:2010:MSR,
  author =       "Luay Nakhleh",
  title =        "A Metric on the Space of Reduced Phylogenetic
                 Networks",
  journal =      j-TCBB,
  volume =       "7",
  number =       "2",
  pages =        "218--222",
  month =        apr,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.2",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri May 7 16:01:51 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Phylogenetic networks are leaf-labeled, rooted,
                 acyclic, and directed graphs that are used to model
                 reticulate evolutionary histories. Several measures for
                 quantifying the topological dissimilarity between two
                 phylogenetic networks have been devised, each of which
                 was proven to be a metric on certain restricted classes
                 of phylogenetic networks. A biologically motivated
                 class of phylogenetic networks, namely, reduced
                 phylogenetic networks, was recently introduced. None of
                 the existing measures is a metric on the space of
                 reduced phylogenetic networks. In this paper, we
                 provide a metric on the space of reduced phylogenetic
                 networks that is computable in time polynomial in the
                 size of the networks.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "indistinguishability; metric.; phylogenetic network;
                 Phylogeny; reduced phylogenetic network",
}

@Article{Gupta:2010:AHD,
  author =       "Gunjan Gupta and Alexander Liu and Joydeep Ghosh",
  title =        "Automated Hierarchical Density Shaving: {A} Robust
                 Automated Clustering and Visualization Framework for
                 Large Biological Data Sets",
  journal =      j-TCBB,
  volume =       "7",
  number =       "2",
  pages =        "223--237",
  month =        apr,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.32",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri May 7 16:01:51 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "A key application of clustering data obtained from
                 sources such as microarrays, protein mass spectroscopy,
                 and phylogenetic profiles is the detection of
                 functionally related genes. Typically, only a small
                 number of functionally related genes cluster into one
                 or more groups, and the rest need to be ignored. For
                 such situations, we present Automated Hierarchical
                 Density Shaving (Auto-HDS), a framework that consists
                 of a fast hierarchical density-based clustering
                 algorithm and an unsupervised model selection strategy.
                 Auto-HDS can automatically select clusters of different
                 densities, present them in a compact hierarchy, and
                 rank individual clusters using an innovative stability
                 criteria. Our framework also provides a simple yet
                 powerful 2D visualization of the hierarchy of clusters
                 that is useful for further interactive exploration. We
                 present results on Gasch and Lee microarray data sets
                 to show the effectiveness of our methods. Additional
                 results on other biological data are included in the
                 supplemental material.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "bioinformatics.; clustering; data and knowledge
                 visualization; Mining methods and algorithms",
}

@Article{Raiford:2010:AIT,
  author =       "Douglas W. Raiford and Dan E. Krane and Travis E. Doom
                 and Michael L. Raymer",
  title =        "Automated Isolation of Translational Efficiency Bias
                 That Resists the Confounding Effect of
                 {GC(AT)}-Content",
  journal =      j-TCBB,
  volume =       "7",
  number =       "2",
  pages =        "238--250",
  month =        apr,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.65",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri May 7 16:01:51 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Genomic sequencing projects are an abundant source of
                 information for biological studies ranging from the
                 molecular to the ecological in scale; however, much of
                 the information present may yet be hidden from casual
                 analysis. One such information domain, trends in codon
                 usage, can provide a wealth of information about an
                 organism's genes and their expression. Degeneracy in
                 the genetic code allows more than one triplet codon to
                 code for the same amino acid, and usage of these codons
                 is often biased such that one or more of these
                 synonymous codons are preferred. Detection of this bias
                 is an important tool in the analysis of genomic data,
                 particularly as a predictor of gene expressivity.
                 Methods for identifying codon usage bias in genomic
                 data that rely solely on genomic sequence data are
                 susceptible to being confounded by the presence of
                 several factors simultaneously influencing codon
                 selection. Presented here is a new technique for
                 removing the effects of one of the more common
                 confounding factors, GC(AT)-content, and of visualizing
                 the search-space for codon usage bias through the use
                 of a solution landscape. This technique successfully
                 isolates expressivity-related codon usage trends, using
                 only genomic sequence information, where other
                 techniques fail due to the presence of GC(AT)-content
                 confounding influences.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "Codon usage bias; GC-content; strand bias;
                 translational efficiency.",
}

@Article{Tenenhaus:2010:GAN,
  author =       "Arthur Tenenhaus and Vincent Guillemot and Xavier
                 Gidrol and Vincent Frouin",
  title =        "Gene Association Networks from Microarray Data Using a
                 Regularized Estimation of Partial Correlation Based on
                 {PLS} Regression",
  journal =      j-TCBB,
  volume =       "7",
  number =       "2",
  pages =        "251--262",
  month =        apr,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.87",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri May 7 16:01:51 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Reconstruction of gene-gene interactions from
                 large-scale data such as microarrays is a first step
                 toward better understanding the mechanisms at work in
                 the cell. Two main issues have to be managed in such a
                 context: (1) choosing which measures have to be used to
                 distinguish between direct and indirect interactions
                 from high-dimensional microarray data and (2)
                 constructing networks with a low proportion of
                 false-positive edges. We present an efficient
                 methodology for the reconstruction of gene interaction
                 networks in a small-sample-size setting. The strength
                 of independence of any two genes is measured, in such
                 `high-dimensional network,' by a regularized estimation
                 of partial correlation based on Partial Least Squares
                 Regression. We finally emphasize specific properties of
                 the proposed method. To assess the sensitivity and
                 specificity of the method, we carried out the
                 reconstruction of networks from simulated data. We also
                 tested PLS-based partial correlation network on static
                 and dynamic real microarray data. An R implementation
                 of the proposed algorithm is available from
                 \path=http://biodev.extra.cea.fr/plspcnetwork/=.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "Gene association networks; high-dimensional data;
                 local false discovery rate.; partial correlation;
                 Partial Least Squares Regression",
}

@Article{Zhu:2010:IFP,
  author =       "Zexuan Zhu and Yew-Soon Ong and Jacek M. Zurada",
  title =        "Identification of Full and Partial Class Relevant
                 Genes",
  journal =      j-TCBB,
  volume =       "7",
  number =       "2",
  pages =        "263--277",
  month =        apr,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.105",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri May 7 16:01:51 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Multiclass cancer classification on microarray data
                 has provided the feasibility of cancer diagnosis across
                 all of the common malignancies in parallel. Using
                 multiclass cancer feature selection approaches, it is
                 now possible to identify genes relevant to a set of
                 cancer types. However, besides identifying the relevant
                 genes for the set of all cancer types, it is deemed to
                 be more informative to biologists if the relevance of
                 each gene to specific cancer or subset of cancer types
                 could be revealed or pinpointed. In this paper, we
                 introduce two new definitions of multiclass relevancy
                 features, i.e., full class relevant (FCR) and partial
                 class relevant (PCR) features. Particularly, FCR
                 denotes genes that serve as candidate biomarkers for
                 discriminating all cancer types. PCR, on the other
                 hand, are genes that distinguish subsets of cancer
                 types. Subsequently, a Markov blanket embedded memetic
                 algorithm is proposed for the simultaneous
                 identification of both FCR and PCR genes. Results
                 obtained on commonly used synthetic and real-world
                 microarray data sets show that the proposed approach
                 converges to valid FCR and PCR genes that would assist
                 biologists in their research work. The identification
                 of both FCR and PCR genes is found to generate
                 improvement in classification accuracy on many
                 microarray data sets. Further comparison study to
                 existing state-of-the-art feature selection algorithms
                 also reveals the effectiveness and efficiency of the
                 proposed approach.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "Bioinformatics; feature/gene selection; Markov
                 blanket.; memetic algorithm; microarray; multiclass
                 cancer classification",
}

@Article{Randhawa:2010:MCM,
  author =       "Ranjit Randhawa and Cliff Shaffer and John Tyson",
  title =        "Model Composition for Macromolecular Regulatory
                 Networks",
  journal =      j-TCBB,
  volume =       "7",
  number =       "2",
  pages =        "278--287",
  month =        apr,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.64",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri May 7 16:01:51 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Models of regulatory networks become more difficult to
                 construct and understand as they grow in size and
                 complexity. Large models are usually built up from
                 smaller models, representing subsets of reactions
                 within the larger network. To assist modelers in this
                 composition process, we present a formal approach for
                 model composition, a wizard-style program for
                 implementing the approach, and suggested language
                 extensions to the Systems Biology Markup Language to
                 support model composition. To illustrate the features
                 of our approach and how to use the JigCell Composition
                 Wizard, we build up a model of the eukaryotic cell
                 cycle ``engine'' from smaller pieces.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "composition; flattening; fusion; Modeling; SBML.",
}

@Article{Bokhari:2010:RNI,
  author =       "Shahid H. Bokhari and Daniel Janies",
  title =        "Reassortment Networks for Investigating the Evolution
                 of Segmented Viruses",
  journal =      j-TCBB,
  volume =       "7",
  number =       "2",
  pages =        "288--298",
  month =        apr,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.73",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri May 7 16:01:51 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Many viruses of interest, such as influenza A, have
                 distinct segments in their genome. The evolution of
                 these viruses involves mutation and reassortment, where
                 segments are interchanged between viruses that coinfect
                 a host. Phylogenetic trees can be constructed to
                 investigate the mutation-driven evolution of individual
                 viral segments. However, reassortment events among
                 viral genomes are not well depicted in such bifurcating
                 trees. We propose the concept of reassortment networks
                 to analyze the evolution of segmented viruses. These
                 are layered graphs in which the layers represent
                 evolutionary stages such as a temporal series of
                 seasons in which influenza viruses are isolated. Nodes
                 represent viral isolates and reassortment events
                 between pairs of isolates. Edges represent evolutionary
                 steps, while weights on edges represent edit costs of
                 reassortment and mutation events. Paths represent
                 possible transformation series among viruses. The
                 length of each path is the sum edit cost of the events
                 required to transform one virus into another. In order
                 to analyze $ \tau $ stages of evolution of $n$ viruses
                 with segments of maximum length $m$, we first compute
                 the pairwise distances between all corresponding
                 segments of all viruses in $ {\cal O}(m^2 n^2) $ time
                 using dynamic programming. The reassortment network,
                 with $ {\cal O}(\tau n^2) $ nodes, is then constructed
                 using these distances. The ancestors and descendents of
                 a specific virus can be traced via shortest paths in
                 this network, which can be found in $ {\cal O}(\tau
                 n^3) $ time.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "dynamic programming; Influenza A; reassortment;
                 segmented virus; shortest paths.",
}

@Article{Bergemann:2010:SQM,
  author =       "Tracy L. Bergemann and Lue Ping Zhao",
  title =        "Signal Quality Measurements for {cDNA} Microarray
                 Data",
  journal =      j-TCBB,
  volume =       "7",
  number =       "2",
  pages =        "299--308",
  month =        apr,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.72",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri May 7 16:01:51 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Concerns about the reliability of expression data from
                 microarrays inspire ongoing research into measurement
                 error in these experiments. Error arises at both the
                 technical level within the laboratory and the
                 experimental level. In this paper, we will focus on
                 estimating the spot-specific error, as there are few
                 currently available models. This paper outlines two
                 different approaches to quantify the reliability of
                 spot-specific intensity estimates. In both cases, the
                 spatial correlation between pixels and its impact on
                 spot quality is accounted for. The first method is a
                 straightforward parametric estimate of within-spot
                 variance that assumes a Gaussian distribution and
                 accounts for spatial correlation via an overdispersion
                 factor. The second method employs a nonparametric
                 quality estimate referred to throughout as the mean
                 square prediction error (MSPE). The MSPE first smoothes
                 a pixel region and then measures the difference between
                 actual pixel values and the smoother. Both methods
                 herein are compared for real and simulated data to
                 assess numerical characteristics and the ability to
                 describe poor spot quality. We conclude that both
                 approaches capture noise in the microarray platform and
                 highlight situations where one method or the other is
                 superior.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "image analysis.; Microarray; prediction error; signal
                 quality",
}

@Article{Blin:2010:ARS,
  author =       "Guillaume Blin and Alain Denise and Serge Dulucq and
                 Claire Herrbach and Heleene Touzet",
  title =        "Alignments of {RNA} Structures",
  journal =      j-TCBB,
  volume =       "7",
  number =       "2",
  pages =        "309--322",
  month =        apr,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.28",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri May 7 16:01:51 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "We describe a theoretical unifying framework to
                 express the comparison of RNA structures, which we call
                 alignment hierarchy. This framework relies on the
                 definition of common supersequences for arc-annotated
                 sequences and encompasses the main existing models for
                 RNA structure comparison based on trees and
                 arc-annotated sequences with a variety of edit
                 operations. It also gives rise to edit models that have
                 not been studied yet. We provide a thorough analysis of
                 the alignment hierarchy, including a new
                 polynomial-time algorithm and an NP-completeness proof.
                 The polynomial-time algorithm involves biologically
                 relevant edit operations such as pairing or unpairing
                 nucleotides. It has been implemented in a software,
                 called {\tt gardenia}, which is available at the Web
                 server \path=http://bioinfo.lifl.fr/RNA/gardenia=.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "algorithm.; arc-annotated sequences; Computational
                 biology; edit distance; NP-hardness; RNA structures",
}

@Article{Jiang:2010:AAP,
  author =       "Minghui Jiang",
  title =        "Approximation Algorithms for Predicting {RNA}
                 Secondary Structures with Arbitrary Pseudoknots",
  journal =      j-TCBB,
  volume =       "7",
  number =       "2",
  pages =        "323--332",
  month =        apr,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.109",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri May 7 16:01:51 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "We study three closely related problems motivated by
                 the prediction of RNA secondary structures with
                 arbitrary pseudoknots: the problem 2-Interval Pattern
                 proposed by Vialette [CHECK END OF SENTENCE], the
                 problem Maximum Base Pair Stackings proposed by Leong
                 et al. [CHECK END OF SENTENCE], and the problem Maximum
                 Stacking Base Pairs proposed by Lyngs. [CHECK END OF
                 SENTENCE]. For the 2-Interval Pattern, we present
                 polynomial-time approximation algorithms for the
                 problem over the preceding-and-crossing model and on
                 input with the unitary restriction. For Maximum Base
                 Pair Stackings and Maximum Stacking Base Pairs, we
                 present polynomial-time approximation algorithms for
                 the two problems on explicit input of candidate base
                 pairs. We also propose a new problem called
                 Length-Weighted Balanced 2-Interval Pattern, which is
                 natural in the context of RNA secondary structure
                 prediction.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "2-intervals.; RNA secondary structure prediction;
                 stacking pairs",
}

@Article{Shibuya:2010:FHD,
  author =       "Tetsuo Shibuya",
  title =        "Fast Hinge Detection Algorithms for Flexible Protein
                 Structures",
  journal =      j-TCBB,
  volume =       "7",
  number =       "2",
  pages =        "333--341",
  month =        apr,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.62",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri May 7 16:01:51 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Analysis of conformational changes is one of the keys
                 to the understanding of protein functions and
                 interactions. For the analysis, we often compare two
                 protein structures, taking flexible regions like hinge
                 regions into consideration. The Root Mean Square
                 Deviation (RMSD) is the most popular measure for
                 comparing two protein structures, but it is only for
                 rigid structures without hinge regions. In this paper,
                 we propose a new measure called RMSD considering hinges
                 (RMSDh) and its variant {\rm RMSDh}$^{(k)}$ for
                 comparing two flexible proteins with hinge regions. We
                 also propose novel efficient algorithms for computing
                 them, which can detect the hinge positions at the same
                 time. The RMSDh is suitable for cases where there is
                 one small hinge region in each of the two target
                 structures. The new algorithm for computing the RMSDh
                 runs in linear time, which is the same as the time
                 complexity for computing the RMSD and is faster than
                 any of previous algorithms for hinge detection. The
                 {\rm RMSDh}$^{(k)}$ is designed for comparing
                 structures with more than one hinge region. The {\rm
                 RMSDh}$^{(k)}$ measure considers at most $k$ small
                 hinge region, i.e., the {\rm RMSDh}$^{(k)}$ value
                 should be small if the two structures are similar
                 except for at most $k$ hinge regions. To compute the
                 value, we propose an $ O(k n^2) $-time and $ O(n)
                 $-space algorithm based on a new dynamic programming
                 technique. With the same computational time and space,
                 we can enumerate the predicted hinge positions. We also
                 test our algorithms against actual flexible protein
                 structures, and show that the hinge positions can be
                 correctly detected by our algorithms.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "Algorithm; dynamic programming.; protein 3D structure
                 comparison; protein hinge detection",
}

@Article{Guillemot:2010:FPT,
  author =       "Sylvain Guillemot and Vincent Berry",
  title =        "Fixed-Parameter Tractability of the Maximum Agreement
                 Supertree Problem",
  journal =      j-TCBB,
  volume =       "7",
  number =       "2",
  pages =        "342--353",
  month =        apr,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.93",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri May 7 16:01:51 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Given a set $L$ of labels and a collection of rooted
                 trees whose leaves are bijectively labeled by some
                 elements of $L$, the Maximum Agreement Supertree
                 (SMAST) problem is given as follows: find a tree $T$ on
                 a largest label set $ L' \subseqeq L $ that
                 homeomorphically contains every input tree restricted
                 to $ L' $. The problem has phylogenetic applications to
                 infer supertrees and perform tree congruence analyses.
                 In this paper, we focus on the parameterized complexity
                 of this NP-hard problem, considering different
                 combinations of parameters as well as particular cases.
                 We show that SMAST on $k$ rooted binary trees on a
                 label set of size $n$ can be solved in $ O((8 n)^k) $
                 time, which is an improvement with respect to the
                 previously known $ O(n^{3k^2}) $ time algorithm. In
                 this case, we also give an $ O((2 k)^p k n^2) $ time
                 algorithm, where $p$ is an upper bound on the number of
                 leaves of $L$ missing in a SMAST solution. This shows
                 that SMAST can be solved efficiently when the input
                 trees are mostly congruent. Then, for the particular
                 case where any triple of leaves is contained in at
                 least one input tree, we give $ O(4^p n^3) $ and $
                 O(3.12^p + n^4) $ time algorithms, obtaining the first
                 fixed-parameter tractable algorithms on a single
                 parameter for this problem. We also obtain
                 intractability results for several combinations of
                 parameters, thus indicating that it is unlikely that
                 fixed-parameter tractable algorithms can be found in
                 these particular cases.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "algorithms; maximum agreement supertree; parameterized
                 complexity; Phylogenetics; reductions; rooted
                 triples.",
}

@Article{Liu:2010:MPI,
  author =       "Xiaowen Liu and Jinyan Li and Lusheng Wang",
  title =        "Modeling Protein Interacting Groups by
                 Quasi-Bicliques: Complexity, Algorithm, and
                 Application",
  journal =      j-TCBB,
  volume =       "7",
  number =       "2",
  pages =        "354--364",
  month =        apr,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.61",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri May 7 16:01:51 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Protein-protein interactions (PPIs) are one of the
                 most important mechanisms in cellular processes. To
                 model protein interaction sites, recent studies have
                 suggested to find interacting protein group pairs from
                 large PPI networks at the first step and then to search
                 conserved motifs within the protein groups to form
                 interacting motif pairs. To consider the noise effect
                 and the incompleteness of biological data, we propose
                 to use quasi-bicliques for finding interacting protein
                 group pairs. We investigate two new problems that arise
                 from finding interacting protein group pairs: the
                 maximum vertex quasi-biclique problem and the maximum
                 balanced quasi-biclique problem. We prove that both
                 problems are NP-hard. This is a surprising result as
                 the widely known maximum vertex biclique problem is
                 polynomial time solvable [1]. We then propose a
                 heuristic algorithm that uses the greedy method to find
                 the quasi-bicliques from PPI networks. Our experiment
                 results on real data show that this algorithm has a
                 better performance than a benchmark algorithm for
                 identifying highly matched BLOCKS and PRINTS motifs. We
                 also report results of two case studies on interacting
                 motif pairs that map well with two interacting domain
                 pairs in iPfam. Availability: The software and
                 supplementary information are available at
                 \path=http://www.cs.cityu.edu.hk/~lwang/software/ppi/index.html=.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "interaction sites; Protein-protein interactions;
                 quasi-bicliques.",
}

@Article{Qi:2010:SGR,
  author =       "Xingqin Qi and Guojun Li and Shuguang Li and Ying Xu",
  title =        "Sorting Genomes by Reciprocal Translocations,
                 Insertions, and Deletions",
  journal =      j-TCBB,
  volume =       "7",
  number =       "2",
  pages =        "365--374",
  month =        apr,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.53",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri May 7 16:01:51 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The problem of sorting by reciprocal translocations
                 (abbreviated as SBT) arises from the field of
                 comparative genomics, which is to find a shortest
                 sequence of reciprocal translocations that transforms
                 one genome $ \Pi $ into another genome $ \Gamma $, with
                 the restriction that $ \Pi $ and $ \Gamma $ contain the
                 same genes. SBT has been proved to be polynomial-time
                 solvable, and several polynomial algorithms have been
                 developed. In this paper, we show how to extend
                 Bergeron's SBT algorithm to include insertions and
                 deletions, allowing to compare genomes containing
                 different genes. In particular, if the gene set of $
                 \Pi $ is a subset (or superset, respectively) of the
                 gene set of $ \Gamma $, we present an approximation
                 algorithm for transforming $ \Pi $ into $ \Gamma $ by
                 reciprocal translocations and deletions (insertions,
                 respectively), providing a sorting sequence with length
                 at most OPT + 2, where OPT is the minimum number of
                 translocations and deletions (insertions, respectively)
                 needed to transform $ \Pi $ into $ \Gamma $; if $ \Pi $
                 and $ \Gamma $ have different genes but not containing
                 each other, we give a heuristic to transform $ \Pi $
                 into $ \Gamma $ by a shortest sequence of reciprocal
                 translocations, insertions, and deletions, with bounds
                 for the length of the sorting sequence it outputs. At a
                 conceptual level, there is some similarity between our
                 algorithm and the algorithm developed by El Mabrouk
                 which is used to sort two chromosomes with different
                 gene contents by reversals, insertions, and
                 deletions.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "algorithm.; deletion; insertion; Translocation",
}

@Article{Unger:2010:LSG,
  author =       "Giora Unger and Benny Chor",
  title =        "Linear Separability of Gene Expression Data Sets",
  journal =      j-TCBB,
  volume =       "7",
  number =       "2",
  pages =        "375--381",
  month =        apr,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.90",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri May 7 16:01:51 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "We study simple geometric properties of gene
                 expression data sets, where samples are taken from two
                 distinct classes (e.g., two types of cancer).
                 Specifically, the problem of linear separability for
                 pairs of genes is investigated. If a pair of genes
                 exhibits linear separation with respect to the two
                 classes, then the joint expression level of the two
                 genes is strongly correlated to the phenomena of the
                 sample being taken from one class or the other. This
                 may indicate an underlying molecular mechanism relating
                 the two genes and the phenomena(e.g., a specific
                 cancer). We developed and implemented novel efficient
                 algorithmic tools for finding all pairs of genes that
                 induce a linear separation of the two sample classes.
                 These tools are based on computational geometric
                 properties and were applied to 10 publicly available
                 cancer data sets. For each data set, we computed the
                 number of actual separating pairs and compared it to an
                 upper bound on the number expected by chance and to the
                 numbers resulting from shuffling the labels of the data
                 at random empirically. Seven out of these 10 data sets
                 are highly separable. Statistically, this phenomenon is
                 highly significant, very unlikely to occur at random.
                 It is therefore reasonable to expect that it manifests
                 a functional association between separating genes and
                 the underlying phenotypic classes.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
  keywords =     "diagnosis; DNA microarrays; Gene expression analysis;
                 linear separation.",
}

@Article{Leitner:2010:OBI,
  author =       "Florian Leitner and Scott A. Mardis and Martin
                 Krallinger and Gianni Cesareni and Lynette A. Hirschman
                 and Alfonso Valencia",
  title =        "An Overview of {BioCreative II.5}",
  journal =      j-TCBB,
  volume =       "7",
  number =       "3",
  pages =        "385--399",
  month =        jul,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.61",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Aug 20 13:49:55 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Kolchinsky:2010:CPP,
  author =       "Artemy Kolchinsky and Alaa Abi-Haidar and Jasleen Kaur
                 and Ahmed Abdeen Hamed and Luis M. Rocha",
  title =        "Classification of Protein-Protein Interaction
                 Full-Text Documents Using Text and Citation Network
                 Features",
  journal =      j-TCBB,
  volume =       "7",
  number =       "3",
  pages =        "400--411",
  month =        jul,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.55",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Aug 20 13:49:55 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Dai:2010:MGN,
  author =       "Hong-Jie Dai and Po-Ting Lai and Richard Tzong-Han
                 Tsai",
  title =        "Multistage Gene Normalization and {SVM}-Based Ranking
                 for Protein Interactor Extraction in Full-Text
                 Articles",
  journal =      j-TCBB,
  volume =       "7",
  number =       "3",
  pages =        "412--420",
  month =        jul,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.45",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Aug 20 13:49:55 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Lan:2010:EIF,
  author =       "Man Lan and Jian Su",
  title =        "Empirical Investigations into Full-Text Protein
                 Interaction Article Categorization Task {(ACT)} in the
                 {BioCreative II.5} Challenge",
  journal =      j-TCBB,
  volume =       "7",
  number =       "3",
  pages =        "421--427",
  month =        jul,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.49",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Aug 20 13:49:55 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Chen:2010:BSI,
  author =       "Yifei Chen and Feng Liu and Bernard Manderick",
  title =        "{BioLMiner} System: Interaction Normalization Task and
                 Interaction Pair Task in the {BioCreative II.5}
                 Challenge",
  journal =      j-TCBB,
  volume =       "7",
  number =       "3",
  pages =        "428--441",
  month =        jul,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.47",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Aug 20 13:49:55 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Saetre:2010:EPI,
  author =       "Rune S{\ae}tre and Kazuhiro Yoshida and Makoto Miwa
                 and Takuya Matsuzaki and Yoshinobu Kano and Jun'ichi
                 Tsujii",
  title =        "Extracting Protein Interactions from Text with the
                 Unified {AkaneRE} Event Extraction System",
  journal =      j-TCBB,
  volume =       "7",
  number =       "3",
  pages =        "442--453",
  month =        jul,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.46",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Aug 20 13:49:55 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Cao:2010:IAM,
  author =       "Yong-gang Cao and Zuofeng Li and Feifan Liu and
                 Shashank Agarwal and Qing Zhang and Hong Yu",
  title =        "An {IR}-Aided Machine Learning Framework for the
                 {BioCreative II.5} Challenge",
  journal =      j-TCBB,
  volume =       "7",
  number =       "3",
  pages =        "454--461",
  month =        jul,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.56",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Aug 20 13:49:55 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Verspoor:2010:ESB,
  author =       "Karin Verspoor and Christophe Roeder and Helen L.
                 Johnson and Kevin Bretonnel Cohen and William A.
                 {Baumgartner, Jr.} and Lawrence E. Hunter",
  title =        "Exploring Species-Based Strategies for Gene
                 Normalization",
  journal =      j-TCBB,
  volume =       "7",
  number =       "3",
  pages =        "462--471",
  month =        jul,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.48",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Aug 20 13:49:55 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Rinaldi:2010:OBI,
  author =       "Fabio Rinaldi and Gerold Schneider and Kaarel
                 Kaljurand and Simon Clematide and Th{\'e}r{\`e}se
                 Vachon and Martin Romacker",
  title =        "{OntoGene} in {BioCreative II.5}",
  journal =      j-TCBB,
  volume =       "7",
  number =       "3",
  pages =        "472--480",
  month =        jul,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.50",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Aug 20 13:49:55 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Hakenberg:2010:EEP,
  author =       "J{\"o}rg Hakenberg and Robert Leaman and Nguyen Ha Vo
                 and Siddhartha Jonnalagadda and Ryan Sullivan and
                 Christopher Miller and Luis Tari and Chitta Baral and
                 Graciela Gonzalez",
  title =        "Efficient Extraction of Protein-Protein Interactions
                 from Full-Text Articles",
  journal =      j-TCBB,
  volume =       "7",
  number =       "3",
  pages =        "481--494",
  month =        jul,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.51",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Aug 20 13:49:55 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Chowdhury:2010:COD,
  author =       "Rezaul Alan Chowdhury and Hai-Son Le and Vijaya
                 Ramachandran",
  title =        "Cache-Oblivious Dynamic Programming for
                 Bioinformatics",
  journal =      j-TCBB,
  volume =       "7",
  number =       "3",
  pages =        "495--510",
  month =        jul,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.94",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Aug 20 13:49:55 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Tininini:2010:CHA,
  author =       "Leonardo Tininini and Paola Bertolazzi and Alessandra
                 Godi and Giuseppe Lancia",
  title =        "{CollHaps}: {A} Heuristic Approach to Haplotype
                 Inference by Parsimony",
  journal =      j-TCBB,
  volume =       "7",
  number =       "3",
  pages =        "511--523",
  month =        jul,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.130",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Aug 20 13:49:55 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Jackups:2010:CAS,
  author =       "Ronald {Jackups, Jr.} and Jie Liang",
  title =        "Combinatorial Analysis for Sequence and Spatial Motif
                 Discovery in Short Sequence Fragments",
  journal =      j-TCBB,
  volume =       "7",
  number =       "3",
  pages =        "524--536",
  month =        jul,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.101",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Aug 20 13:49:55 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Han:2010:NPC,
  author =       "Xiaoxu Han",
  title =        "Nonnegative Principal Component Analysis for Cancer
                 Molecular Pattern Discovery",
  journal =      j-TCBB,
  volume =       "7",
  number =       "3",
  pages =        "537--549",
  month =        jul,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.36",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Aug 20 13:49:55 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Zeng:2010:SSC,
  author =       "Jia Zeng and Xiao-Yu Zhao and Xiao-Qin Cao and Hong
                 Yan",
  title =        "{SCS}: Signal, Context, and Structure Features for
                 Genome-Wide Human Promoter Recognition",
  journal =      j-TCBB,
  volume =       "7",
  number =       "3",
  pages =        "550--562",
  month =        jul,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.95",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Aug 20 13:49:55 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Zimek:2010:SHF,
  author =       "Arthur Zimek and Fabian Buchwald and Eibe Frank and
                 Stefan Kramer",
  title =        "A Study of Hierarchical and Flat Classification of
                 Proteins",
  journal =      j-TCBB,
  volume =       "7",
  number =       "3",
  pages =        "563--571",
  month =        jul,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.104",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Aug 20 13:49:55 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Bonet:2010:CUD,
  author =       "Maria Luisa Bonet and Katherine {St. John}",
  title =        "On the Complexity of {uSPR} Distance",
  journal =      j-TCBB,
  volume =       "7",
  number =       "3",
  pages =        "572--576",
  month =        jul,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.132",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Aug 20 13:49:55 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Mandou:2010:GEI,
  author =       "Ion Mandou and Giri Narasimhan and Yi Pan and Yanqing
                 Zhang",
  title =        "{Guest Editors}' Introduction to the Special Section
                 on Bioinformatics Research and Applications",
  journal =      j-TCBB,
  volume =       "7",
  number =       "4",
  pages =        "577--578",
  month =        oct,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.114",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:02 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Munoz:2010:RPG,
  author =       "Adriana Munoz and David Sankoff",
  title =        "Rearrangement Phylogeny of Genomes in Contig Form",
  journal =      j-TCBB,
  volume =       "7",
  number =       "4",
  pages =        "579--587",
  month =        oct,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.66",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:02 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Venkatachalam:2010:UTC,
  author =       "Balaji Venkatachalam and Jim Apple and Katherine St.
                 John and Daniel Gusfield",
  title =        "Untangling Tanglegrams: Comparing Trees by Their
                 Drawings",
  journal =      j-TCBB,
  volume =       "7",
  number =       "4",
  pages =        "588--597",
  month =        oct,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.57",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:02 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Bonizzoni:2010:PPX,
  author =       "Paola Bonizzoni and Gianluca Della Vedova and Riccardo
                 Dondi and Yuri Pirola and Romeo Rizzi",
  title =        "Pure Parsimony Xor Haplotyping",
  journal =      j-TCBB,
  volume =       "7",
  number =       "4",
  pages =        "598--610",
  month =        oct,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.52",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:02 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Wu:2010:ECC,
  author =       "Yufeng Wu",
  title =        "Exact Computation of Coalescent Likelihood for
                 Panmictic and Subdivided Populations under the Infinite
                 Sites Model",
  journal =      j-TCBB,
  volume =       "7",
  number =       "4",
  pages =        "611--618",
  month =        oct,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.2",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:02 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Rajasekaran:2010:IAP,
  author =       "Sanguthevar Rajasekaran and Sahar {Al Seesi} and Reda
                 A. Ammar",
  title =        "Improved Algorithms for Parsing {ESLTAGs}: {A}
                 Grammatical Model Suitable for {RNA} Pseudoknots",
  journal =      j-TCBB,
  volume =       "7",
  number =       "4",
  pages =        "619--627",
  month =        oct,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.54",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:02 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Blin:2010:QGP,
  author =       "Guillaume Blin and Florian Sikora and Stephane
                 Vialette",
  title =        "Querying Graphs in Protein-Protein Interactions
                 Networks Using Feedback Vertex Set",
  journal =      j-TCBB,
  volume =       "7",
  number =       "4",
  pages =        "628--635",
  month =        oct,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.53",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:02 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Cheng:2010:SLM,
  author =       "Qiang Cheng",
  title =        "A Sparse Learning Machine for High-Dimensional Data
                 with Application to Microarray Gene Analysis",
  journal =      j-TCBB,
  volume =       "7",
  number =       "4",
  pages =        "636--646",
  month =        oct,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.8",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:02 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Langdon:2010:SSD,
  author =       "W. B. Langdon and G. J. G. Upton and R. da Silva
                 Camargo and A. P. Harrison",
  title =        "A Survey of Spatial Defects in {Homo Sapiens
                 Affymetrix GeneChips}",
  journal =      j-TCBB,
  volume =       "7",
  number =       "4",
  pages =        "647--653",
  month =        oct,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.108",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:02 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Li:2010:CRA,
  author =       "Gang Li and Tak-Ming Chan and Kwong-Sak Leung and
                 Kin-Hong Lee",
  title =        "A Cluster Refinement Algorithm for Motif Discovery",
  journal =      j-TCBB,
  volume =       "7",
  number =       "4",
  pages =        "654--668",
  month =        oct,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.25",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:02 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Zhou:2010:FNN,
  author =       "Jianjun Zhou and Jorg Sander and Zhipeng Cai and
                 Lusheng Wang and Guohui Lin",
  title =        "Finding the Nearest Neighbors in Biological Databases
                 Using Less Distance Computations",
  journal =      j-TCBB,
  volume =       "7",
  number =       "4",
  pages =        "669--680",
  month =        oct,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.99",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:02 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Huang:2010:HRR,
  author =       "Liang-Tsung Huang and Lien-Fu Lai and M. Michael
                 Gromiha",
  title =        "Human-Readable Rule Generator for Integrating Amino
                 Acid Sequence Information and Stability of Mutant
                 Proteins",
  journal =      j-TCBB,
  volume =       "7",
  number =       "4",
  pages =        "681--687",
  month =        oct,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.128",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:02 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Godin:2010:QDS,
  author =       "Christophe Godin and Pascal Ferraro",
  title =        "Quantifying the Degree of Self-Nestedness of Trees:
                 Application to the Structural Analysis of Plants",
  journal =      j-TCBB,
  volume =       "7",
  number =       "4",
  pages =        "688--703",
  month =        oct,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.29",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:02 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Snir:2010:QMD,
  author =       "Sagi Snir and Satish Rao",
  title =        "Quartets {MaxCut}: {A} Divide and Conquer Quartets
                 Algorithm",
  journal =      j-TCBB,
  volume =       "7",
  number =       "4",
  pages =        "704--718",
  month =        oct,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.133",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:02 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Lu:2010:RNM,
  author =       "Xin Lu and Anthony Gamst and Ronghui Xu",
  title =        "{RDCurve}: {A} Nonparametric Method to Evaluate the
                 Stability of Ranking Procedures",
  journal =      j-TCBB,
  volume =       "7",
  number =       "4",
  pages =        "719--726",
  month =        oct,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.138",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:02 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Tsang:2010:SPA,
  author =       "Herbert H. Tsang and Kay C. Wiese",
  title =        "{SARNA-Predict}: Accuracy Improvement of {RNA}
                 Secondary Structure Prediction Using Permutation-Based
                 Simulated Annealing",
  journal =      j-TCBB,
  volume =       "7",
  number =       "4",
  pages =        "727--740",
  month =        oct,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.97",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:02 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{You:2010:UGP,
  author =       "Liwen You and Vladimir Brusic and Marcus Gallagher and
                 Mikael Boden",
  title =        "Using {Gaussian} Process with Test Rejection to Detect
                 {T}-Cell Epitopes in Pathogen Genomes",
  journal =      j-TCBB,
  volume =       "7",
  number =       "4",
  pages =        "741--751",
  month =        oct,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.131",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:02 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Apostolico:2010:VDE,
  author =       "Alberto Apostolico and Matteo Comin and Laxmi Parida",
  title =        "{VARUN}: Discovering Extensible Motifs under
                 Saturation Constraints",
  journal =      j-TCBB,
  volume =       "7",
  number =       "4",
  pages =        "752--762",
  month =        oct,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.123",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:02 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Miklos:2010:MPI,
  author =       "Istvan Miklos and Bence Melykuti and Krister Swenson",
  title =        "The {Metropolized} Partial Importance Sampling {MCMC}
                 Mixes Slowly on Minimum Reversal Rearrangement Paths",
  journal =      j-TCBB,
  volume =       "7",
  number =       "4",
  pages =        "763--767",
  month =        oct,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.26",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:02 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Anonymous:2010:TAI,
  author =       "Anonymous",
  title =        "2010 {TCBB} Annual Index",
  journal =      j-TCBB,
  volume =       "7",
  number =       "4",
  pages =        "763--767",
  month =        oct,
  year =         "2010",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.116",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:02 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Sagot:2011:EEa,
  author =       "Marie-France Sagot",
  title =        "{EIC} Editorial",
  journal =      j-TCBB,
  volume =       "8",
  number =       "1",
  pages =        "1--1",
  month =        jan,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.7",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:04 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Owen:2011:FAC,
  author =       "Megan Owen and J. Scott Provan",
  title =        "A Fast Algorithm for Computing Geodesic Distances in
                 Tree Space",
  journal =      j-TCBB,
  volume =       "8",
  number =       "1",
  pages =        "2--13",
  month =        jan,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.3",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:04 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Comparing and computing distances between phylogenetic
                 trees are important biological problems, especially for
                 models where edge lengths play an important role. The
                 geodesic distance measure between two phylogenetic
                 trees with edge lengths is the length of the shortest
                 path between them in the continuous tree space
                 introduced by Billera, Holmes, and Vogtmann. This tree
                 space provides a powerful tool for studying and
                 comparing phylogenetic trees, both in exhibiting a
                 natural distance measure and in providing a
                 Euclidean-like structure for solving optimization
                 problems on trees. An important open problem is to find
                 a polynomial time algorithm for finding geodesics in
                 tree space.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Shah:2011:GFA,
  author =       "Mohak Shah and Jacques Corbeil",
  title =        "A General Framework for Analyzing Data from Two Short
                 Time-Series Microarray Experiments",
  journal =      j-TCBB,
  volume =       "8",
  number =       "1",
  pages =        "14--26",
  month =        jan,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.51",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:04 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "We propose a general theoretical framework for
                 analyzing differentially expressed genes and behavior
                 patterns from two homogeneous short time-course data.
                 The framework generalizes the recently proposed
                 Hilbert--Schmidt Independence Criterion (HSIC)-based
                 framework adapting it to the time-series scenario by
                 utilizing tensor analysis for data transformation. The
                 proposed framework is effective in yielding criteria
                 that can identify both the differentially expressed
                 genes and time-course patterns of interest between two
                 time-series experiments without requiring to explicitly
                 cluster the data.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Mauch:2011:EFE,
  author =       "Sean Mauch and Mark Stalzer",
  title =        "Efficient Formulations for Exact Stochastic Simulation
                 of Chemical Systems",
  journal =      j-TCBB,
  volume =       "8",
  number =       "1",
  pages =        "27--35",
  month =        jan,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.47",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:04 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "One can generate trajectories to simulate a system of
                 chemical reactions using either Gillespie's direct
                 method or Gibson and Bruck's next reaction method.
                 Because one usually needs many trajectories to
                 understand the dynamics of a system, performance is
                 important. In this paper, we present new formulations
                 of these methods that improve the computational
                 complexity of the algorithms. We present optimized
                 implementations, available from
                 \path=http://cain.sourceforge.net/=, that offer better
                 performance than previous work. There is no single
                 method that is best for all problems. Simple
                 formulations often work best for systems with a small
                 number of reactions, while some sophisticated methods
                 offer the best performance for large problems and scale
                 well asymptotically.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Gasbarra:2011:EHF,
  author =       "Dario Gasbarra and Sangita Kulathinal and Matti
                 Pirinen and Mikko J. Sillanpaa",
  title =        "Estimating Haplotype Frequencies by Combining Data
                 from Large {DNA} Pools with Database Information",
  journal =      j-TCBB,
  volume =       "8",
  number =       "1",
  pages =        "36--44",
  month =        jan,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.71",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:04 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "We assume that allele frequency data have been
                 extracted from several large DNA pools, each containing
                 genetic material of up to hundreds of sampled
                 individuals. Our goal is to estimate the haplotype
                 frequencies among the sampled individuals by combining
                 the pooled allele frequency data with prior knowledge
                 about the set of possible haplotypes. Such prior
                 information can be obtained, for example, from a
                 database such as HapMap. We present a Bayesian
                 haplotyping method for pooled DNA based on a continuous
                 approximation of the multinomial distribution. The
                 proposed method is applicable when the sizes of the DNA
                 pools and/or the number of considered loci exceed the
                 limits of several earlier methods.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Bajaj:2011:FFP,
  author =       "Chandrajit L. Bajaj and Rezaul Chowdhury and Vinay
                 Siddahanavalli",
  title =        "{$ F^2 $Dock}: Fast {Fourier} Protein-Protein
                 Docking",
  journal =      j-TCBB,
  volume =       "8",
  number =       "1",
  pages =        "45--58",
  month =        jan,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.57",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:04 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The functions of proteins are often realized through
                 their mutual interactions. Determining a relative
                 transformation for a pair of proteins and their
                 conformations which form a stable complex, reproducible
                 in nature, is known as docking. It is an important step
                 in drug design, structure determination, and
                 understanding function and structure relationships. In
                 this paper, we extend our nonuniform fast Fourier
                 transform-based docking algorithm to include an
                 adaptive search phase (both translational and
                 rotational) and thereby speed up its execution. We have
                 also implemented a multithreaded version of the
                 adaptive docking algorithm for even faster execution on
                 multicore machines. We call this protein-protein
                 docking code $ F^2 $Dock ($ F^2 $ = {\rm
                 \underline{F}ast\underline{F}ourier}).",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Giard:2011:FSB,
  author =       "Joachim Giard and Patrice Rondao Alface and Jean-Luc
                 Gala and Benoit Macq",
  title =        "Fast Surface-Based Travel Depth Estimation Algorithm
                 for Macromolecule Surface Shape Description",
  journal =      j-TCBB,
  volume =       "8",
  number =       "1",
  pages =        "59--68",
  month =        jan,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.53",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:04 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Travel Depth, introduced by Coleman and Sharp in 2006,
                 is a physical interpretation of molecular depth, a term
                 frequently used to describe the shape of a molecular
                 active site or binding site. Travel Depth can be seen
                 as the physical distance a solvent molecule would have
                 to travel from a point of the surface, i.e., the
                 Solvent-Excluded Surface (SES), to its convex hull.
                 Existing algorithms providing an estimation of the
                 Travel Depth are based on a regular sampling of the
                 molecule volume and the use of the Dijkstra's shortest
                 path algorithm. Since Travel Depth is only defined on
                 the molecular surface, this volume-based approach is
                 characterized by a large computational complexity due
                 to the processing of unnecessary samples lying inside
                 or outside the molecule.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Pizzi:2011:FSM,
  author =       "Cinzia Pizzi and Pasi Rastas and Esko Ukkonen",
  title =        "Finding Significant Matches of Position Weight
                 Matrices in Linear Time",
  journal =      j-TCBB,
  volume =       "8",
  number =       "1",
  pages =        "69--79",
  month =        jan,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.35",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:04 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Position weight matrices are an important method for
                 modeling signals or motifs in biological sequences,
                 both in DNA and protein contexts. In this paper, we
                 present fast algorithms for the problem of finding
                 significant matches of such matrices. Our algorithms
                 are of the online type, and they generalize classical
                 multipattern matching, filtering, and superalphabet
                 techniques of combinatorial string matching to the
                 problem of weight matrix matching. Several variants of
                 the algorithms are developed, including multiple matrix
                 extensions that perform the search for several matrices
                 in one scan through the sequence database. Experimental
                 performance evaluation is provided to compare the new
                 techniques against each other as well as against some
                 other online and index-based algorithms proposed in the
                 literature.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Andonie:2011:FAP,
  author =       "Razvan Andonie and Levente Fabry-Asztalos and
                 Christopher B. Abdul-Wahid and Sarah Abdul-Wahid and
                 Grant I. Barker and Lukas C. Magill",
  title =        "Fuzzy {ARTMAP} Prediction of Biological Activities for
                 Potential {HIV-1} Protease Inhibitors Using a Small
                 Molecular Data Set",
  journal =      j-TCBB,
  volume =       "8",
  number =       "1",
  pages =        "80--93",
  month =        jan,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.50",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:04 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Obtaining satisfactory results with neural networks
                 depends on the availability of large data samples. The
                 use of small training sets generally reduces
                 performance. Most classical Quantitative
                 Structure-Activity Relationship (QSAR) studies for a
                 specific enzyme system have been performed on small
                 data sets. We focus on the neuro-fuzzy prediction of
                 biological activities of HIV-1 protease inhibitory
                 compounds when inferring from small training sets. We
                 propose two computational intelligence prediction
                 techniques which are suitable for small training sets,
                 at the expense of some computational overhead. Both
                 techniques are based on the FAMR model. The FAMR is a
                 Fuzzy ARTMAP (FAM) incremental learning system used for
                 classification and probability estimation.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Mitra:2011:GNS,
  author =       "Sushmita Mitra and Ranajit Das and Yoichi Hayashi",
  title =        "Genetic Networks and Soft Computing",
  journal =      j-TCBB,
  volume =       "8",
  number =       "1",
  pages =        "94--107",
  month =        jan,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.39",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:04 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The analysis of gene regulatory networks provides
                 enormous information on various fundamental cellular
                 processes involving growth, development, hormone
                 secretion, and cellular communication. Their extraction
                 from available gene expression profiles is a
                 challenging problem. Such reverse engineering of
                 genetic networks offers insight into cellular activity
                 toward prediction of adverse effects of new drugs or
                 possible identification of new drug targets. Tasks such
                 as classification, clustering, and feature selection
                 enable efficient mining of knowledge about gene
                 interactions in the form of networks. It is known that
                 biological data is prone to different kinds of noise
                 and ambiguity. Soft computing tools, such as fuzzy
                 sets, evolutionary strategies, and neurocomputing, have
                 been found to be helpful in providing low-cost,
                 acceptable solutions in the presence of various types
                 of uncertainties.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Wang:2011:IMG,
  author =       "Wenxue Wang and Bijoy K. Ghosh and Himadri Pakrasi",
  title =        "Identification and Modeling of Genes with Diurnal
                 Oscillations from Microarray Time Series Data",
  journal =      j-TCBB,
  volume =       "8",
  number =       "1",
  pages =        "108--121",
  month =        jan,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.37",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:04 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Behavior of living organisms is strongly modulated by
                 the day and night cycle giving rise to a cyclic pattern
                 of activities. Such a pattern helps the organisms to
                 coordinate their activities and maintain a balance
                 between what could be performed during the ``day'' and
                 what could be relegated to the ``night.'' This cyclic
                 pattern, called the ``Circadian Rhythm,'' is a
                 biological phenomenon observed in a large number of
                 organisms. In this paper, our goal is to analyze
                 transcriptome data from Cyanothece for the purpose of
                 discovering genes whose expressions are rhythmic. We
                 cluster these genes into groups that are close in terms
                 of their phases and show that genes from a specific
                 metabolic functional category are tightly clustered,
                 indicating perhaps a ``preferred time of the
                 day/night'' when the organism performs this function.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Luo:2011:ICE,
  author =       "Lin-Kai Luo and Deng-Feng Huang and Ling-Jun Ye and
                 Qi-Feng Zhou and Gui-Fang Shao and Hong Peng",
  title =        "Improving the Computational Efficiency of Recursive
                 Cluster Elimination for Gene Selection",
  journal =      j-TCBB,
  volume =       "8",
  number =       "1",
  pages =        "122--129",
  month =        jan,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.44",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:04 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The gene expression data are usually provided with a
                 large number of genes and a relatively small number of
                 samples, which brings a lot of new challenges.
                 Selecting those informative genes becomes the main
                 issue in microarray data analysis. Recursive cluster
                 elimination based on support vector machine (SVM-RCE)
                 has shown the better classification accuracy on some
                 microarray data sets than recursive feature elimination
                 based on support vector machine (SVM-RFE). However,
                 SVM-RCE is extremely time-consuming. In this paper, we
                 propose an improved method of SVM-RCE called ISVM-RCE.
                 ISVM-RCE first trains a SVM model with all clusters,
                 then applies the infinite norm of weight coefficient
                 vector in each cluster to score the cluster, finally
                 eliminates the gene clusters with the lowest score.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Tan:2011:IPK,
  author =       "Mehmet Tan and Mohammed Alshalalfa and Reda Alhajj and
                 Faruk Polat",
  title =        "Influence of Prior Knowledge in Constraint-Based
                 Learning of Gene Regulatory Networks",
  journal =      j-TCBB,
  volume =       "8",
  number =       "1",
  pages =        "130--142",
  month =        jan,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.58",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:04 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Constraint-based structure learning algorithms
                 generally perform well on sparse graphs. Although
                 sparsity is not uncommon, there are some domains where
                 the underlying graph can have some dense regions; one
                 of these domains is gene regulatory networks, which is
                 the main motivation to undertake the study described in
                 this paper. We propose a new constraint-based algorithm
                 that can both increase the quality of output and
                 decrease the computational requirements for learning
                 the structure of gene regulatory networks. The
                 algorithm is based on and extends the PC algorithm. Two
                 different types of information are derived from the
                 prior knowledge; one is the probability of existence of
                 edges, and the other is the nodes that seem to be
                 dependent on a large number of nodes compared to other
                 nodes in the graph.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Gong:2011:ITM,
  author =       "Liuling Gong and Nidhal Bouaynaya and Dan Schonfeld",
  title =        "Information-Theoretic Model of Evolution over Protein
                 Communication Channel",
  journal =      j-TCBB,
  volume =       "8",
  number =       "1",
  pages =        "143--151",
  month =        jan,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.1",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:04 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "In this paper, we propose a communication model of
                 evolution and investigate its information-theoretic
                 bounds. The process of evolution is modeled as the
                 retransmission of information over a protein
                 communication channel, where the transmitted message is
                 the organism's proteome encoded in the DNA. We compute
                 the capacity and the rate distortion functions of the
                 protein communication system for the three domains of
                 life: Archaea, Bacteria, and Eukaryotes. The tradeoff
                 between the transmission rate and the distortion in
                 noisy protein communication channels is analyzed. As
                 expected, comparison between the optimal transmission
                 rate and the channel capacity indicates that the
                 biological fidelity does not reach the Shannon optimal
                 distortion.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Barker:2011:LGR,
  author =       "Nathan A. Barker and Chris J. Myers and Hiroyuki
                 Kuwahara",
  title =        "Learning Genetic Regulatory Network Connectivity from
                 Time Series Data",
  journal =      j-TCBB,
  volume =       "8",
  number =       "1",
  pages =        "152--165",
  month =        jan,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.48",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:04 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Recent experimental advances facilitate the collection
                 of time series data that indicate which genes in a cell
                 are expressed. This information can be used to
                 understand the genetic regulatory network that
                 generates the data. Typically, Bayesian analysis
                 approaches are applied which neglect the time series
                 nature of the experimental data, have difficulty in
                 determining the direction of causality, and do not
                 perform well on networks with tight feedback. To
                 address these problems, this paper presents a method to
                 learn genetic network connectivity which exploits the
                 time series nature of experimental data to achieve
                 better causal predictions. This method first breaks up
                 the data into bins.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Ropers:2011:MRU,
  author =       "Delphine Ropers and Valentina Baldazzi and Hidde de
                 Jong",
  title =        "Model Reduction Using Piecewise-Linear Approximations
                 Preserves Dynamic Properties of the Carbon Starvation
                 Response in {{\em Escherichia coli}}",
  journal =      j-TCBB,
  volume =       "8",
  number =       "1",
  pages =        "166--181",
  month =        jan,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.49",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:04 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The adaptation of the bacterium Escherichia coli to
                 carbon starvation is controlled by a large network of
                 biochemical reactions involving genes, mRNAs, proteins,
                 and signalling molecules. The dynamics of these
                 networks is difficult to analyze, notably due to a lack
                 of quantitative information on parameter values. To
                 overcome these limitations, model reduction approaches
                 based on quasi-steady-state (QSS) and piecewise-linear
                 (PL) approximations have been proposed, resulting in
                 models that are easier to handle mathematically and
                 computationally. These approximations are not supposed
                 to affect the capability of the model to account for
                 essential dynamical properties of the system, but the
                 validity of this assumption has not been systematically
                 tested.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Wu:2011:NMI,
  author =       "Yufeng Wu",
  title =        "New Methods for Inference of Local Tree Topologies
                 with Recombinant {SNP} Sequences in Populations",
  journal =      j-TCBB,
  volume =       "8",
  number =       "1",
  pages =        "182--193",
  month =        jan,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.27",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:04 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Large amount of population-scale genetic variation
                 data are being collected in populations. One
                 potentially important biological problem is to infer
                 the population genealogical history from these genetic
                 variation data. Partly due to recombination,
                 genealogical history of a set of DNA sequences in a
                 population usually cannot be represented by a single
                 tree. Instead, genealogy is better represented by a
                 genealogical network, which is a compact representation
                 of a set of correlated local genealogical trees, each
                 for a short region of genome and possibly with
                 different topology. Inference of genealogical history
                 for a set of DNA sequences under recombination has many
                 potential applications, including association mapping
                 of complex diseases.In this paper, we present two new
                 methods for reconstructing local tree topologies with
                 the presence of recombination, which extend and improve
                 the previous work in.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Agrawal:2011:PSS,
  author =       "Ankit Agrawal and Xiaoqiu Huang",
  title =        "Pairwise Statistical Significance of Local Sequence
                 Alignment Using Sequence-Specific and Position-Specific
                 Substitution Matrices",
  journal =      j-TCBB,
  volume =       "8",
  number =       "1",
  pages =        "194--205",
  month =        jan,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.69",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:04 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Pairwise sequence alignment is a central problem in
                 bioinformatics, which forms the basis of various other
                 applications. Two related sequences are expected to
                 have a high alignment score, but relatedness is usually
                 judged by statistical significance rather than by
                 alignment score. Recently, it was shown that pairwise
                 statistical significance gives promising results as an
                 alternative to database statistical significance for
                 getting individual significance estimates of pairwise
                 alignment scores. The improvement was mainly attributed
                 to making the statistical significance estimation
                 process more sequence-specific and
                 database-independent. In this paper, we use
                 sequence-specific and position-specific substitution
                 matrices to derive the estimates of pairwise
                 statistical significance, which is expected to use more
                 sequence-specific information in estimating pairwise
                 statistical significance.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{vanBerlo:2011:PMF,
  author =       "Rogier J. P. van Berlo and Dick de Ridder and
                 Jean-Marc Daran and Pascale A. S. Daran-Lapujade and
                 Bas Teusink and Marcel J. T. Reinders",
  title =        "Predicting Metabolic Fluxes Using Gene Expression
                 Differences As Constraints",
  journal =      j-TCBB,
  volume =       "8",
  number =       "1",
  pages =        "206--216",
  month =        jan,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.55",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:04 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "A standard approach to estimate intracellular fluxes
                 on a genome-wide scale is flux-balance analysis (FBA),
                 which optimizes an objective function subject to
                 constraints on (relations between) fluxes. The
                 performance of FBA models heavily depends on the
                 relevance of the formulated objective function and the
                 completeness of the defined constraints. Previous
                 studies indicated that FBA predictions can be improved
                 by adding regulatory on/off constraints. These
                 constraints were imposed based on either absolute or
                 relative gene expression values. We provide a new
                 algorithm that directly uses regulatory up/down
                 constraints based on gene expression data in FBA
                 optimization (tFBA). Our assumption is that if the
                 activity of a gene drastically changes from one
                 condition to the other, the flux through the reaction
                 controlled by that gene will change accordingly.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Lahti:2011:PAP,
  author =       "Leo Lahti and Laura L. Elo and Tero Aittokallio and
                 Samuel Kaski",
  title =        "Probabilistic Analysis of Probe Reliability in
                 Differential Gene Expression Studies with Short
                 Oligonucleotide Arrays",
  journal =      j-TCBB,
  volume =       "8",
  number =       "1",
  pages =        "217--225",
  month =        jan,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.38",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:04 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Probe defects are a major source of noise in gene
                 expression studies. While existing approaches detect
                 noisy probes based on external information such as
                 genomic alignments, we introduce and validate a
                 targeted probabilistic method for analyzing probe
                 reliability directly from expression data and
                 independently of the noise source. This provides
                 insights into the various sources of probe-level noise
                 and gives tools to guide probe design.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Kc:2011:TIP,
  author =       "Dukka B. Kc and Dennis R. Livesay",
  title =        "Topology Improves Phylogenetic Motif Functional Site
                 Predictions",
  journal =      j-TCBB,
  volume =       "8",
  number =       "1",
  pages =        "226--233",
  month =        jan,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.60",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:04 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Prediction of protein functional sites from
                 sequence-derived data remains an open bioinformatics
                 problem. We have developed a phylogenetic motif (PM)
                 functional site prediction approach that identifies
                 functional sites from alignment fragments that parallel
                 the evolutionary patterns of the family. In our
                 approach, PMs are identified by comparing tree
                 topologies of each alignment fragment to that of the
                 complete phylogeny. Herein, we bypass the phylogenetic
                 reconstruction step and identify PMs directly from
                 distance matrix comparisons. In order to optimize the
                 new algorithm, we consider three different distance
                 matrices and 13 different matrix similarity scores. We
                 assess the performance of the various approaches on a
                 structurally nonredundant data set that includes three
                 types of functional site definitions.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Hoque:2011:TRG,
  author =       "Md Tamjidul Hoque and Madhu Chetty and Andrew Lewis
                 and Abdul Sattar",
  title =        "Twin Removal in Genetic Algorithms for Protein
                 Structure Prediction Using Low-Resolution Model",
  journal =      j-TCBB,
  volume =       "8",
  number =       "1",
  pages =        "234--245",
  month =        jan,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.34",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:04 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "This paper presents the impact of twins and the
                 measures for their removal from the population of
                 genetic algorithm (GA) when applied to effective
                 conformational searching. It is conclusively shown that
                 a twin removal strategy for a GA provides considerably
                 enhanced performance when investigating solutions to
                 complex ab initio protein structure prediction (PSP)
                 problems in low-resolution model. Without twin removal,
                 GA crossover and mutation operations can become
                 ineffectual as generations lose their ability to
                 produce significant differences, which can lead to the
                 solution stalling.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{daCosta:2011:WPC,
  author =       "Joaquim F. Pinto da Costa and Hugo Alonso and Luis
                 Roque",
  title =        "A Weighted Principal Component Analysis and Its
                 Application to Gene Expression Data",
  journal =      j-TCBB,
  volume =       "8",
  number =       "1",
  pages =        "246--252",
  month =        jan,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.61",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:04 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "In this work, we introduce in the first part new
                 developments in Principal Component Analysis (PCA) and
                 in the second part a new method to select variables
                 (genes in our application). Our focus is on problems
                 where the values taken by each variable do not all have
                 the same importance and where the data may be
                 contaminated with noise and contain outliers, as is the
                 case with microarray data. The usual PCA is not
                 appropriate to deal with this kind of problems. In this
                 context, we propose the use of a new correlation
                 coefficient as an alternative to Pearson's. This leads
                 to a so-called weighted PCA (WPCA).",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Li:2011:DAN,
  author =       "Ping Li and James Lam",
  title =        "Disturbance Analysis of Nonlinear Differential
                 Equation Models of Genetic {SUM} Regulatory Networks",
  journal =      j-TCBB,
  volume =       "8",
  number =       "1",
  pages =        "253--259",
  month =        jan,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.19",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:04 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Noise disturbances and time delays are frequently met
                 in cellular genetic regulatory systems. This paper is
                 concerned with the disturbance analysis of a class of
                 genetic regulatory networks described by nonlinear
                 differential equation models. The mechanisms of genetic
                 regulatory networks to amplify (attenuate) external
                 disturbance are explored, and a simple measure of the
                 amplification (attenuation) level is developed from a
                 nonlinear robust control point of view. It should be
                 noted that the conditions used to measure the
                 disturbance level are delay-independent or
                 delay-dependent, and are expressed within the framework
                 of linear matrix inequalities, which can be
                 characterized as convex optimization, and computed by
                 the interior-point algorithm easily.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Luo:2011:LTA,
  author =       "Cheng-Wei Luo and Ming-Chiang Chen and Yi-Ching Chen
                 and Roger W. L. Yang and Hsiao-Fei Liu and Kun-Mao
                 Chao",
  title =        "Linear-Time Algorithms for the Multiple Gene
                 Duplication Problems",
  journal =      j-TCBB,
  volume =       "8",
  number =       "1",
  pages =        "260--265",
  month =        jan,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.52",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:04 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "A fundamental problem arising in the evolutionary
                 molecular biology is to discover the locations of gene
                 duplications and multiple gene duplication episodes
                 based on the phylogenetic information. The solutions to
                 the MULTIPLE GENE DUPLICATION problems can provide
                 useful clues to place the gene duplication events onto
                 the locations of a species tree and to expose the
                 multiple gene duplication episodes. In this paper, we
                 study two variations of the MULTIPLE GENE DUPLICATION
                 problems: the EPISODE-CLUSTERING (EC) problem and the
                 MINIMUM EPISODES (ME) problem. For the EC problem, we
                 improve the results of Burleigh et al. with an optimal
                 linear-time algorithm.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Mao:2011:RMS,
  author =       "Kezhi Z. Mao and Wenyin Tang",
  title =        "Recursive {Mahalanobis} Separability Measure for Gene
                 Subset Selection",
  journal =      j-TCBB,
  volume =       "8",
  number =       "1",
  pages =        "266--272",
  month =        jan,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.43",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:04 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Mahalanobis class separability measure provides an
                 effective evaluation of the discriminative power of a
                 feature subset, and is widely used in feature
                 selection. However, this measure is computationally
                 intensive or even prohibitive when it is applied to
                 gene expression data. In this study, a recursive
                 approach to Mahalanobis measure evaluation is proposed,
                 with the goal of reducing computational overhead.
                 Instead of evaluating Mahalanobis measure directly in
                 high-dimensional space, the recursive approach
                 evaluates the measure through successive evaluations in
                 2D space. Because of its recursive nature, this
                 approach is extremely efficient when it is combined
                 with a forward search procedure. In addition, it is
                 noted that gene subsets selected by Mahalanobis measure
                 tend to overfit training data and generalize
                 unsatisfactorily on unseen test data, due to small
                 sample size in gene expression problems.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Krishnamurthy:2011:SMM,
  author =       "Vikram Krishnamurthy and Kai-Yiu Luk",
  title =        "Semi-{Markov} Models for {Brownian} Dynamics
                 Permeation in Biological Ion Channels",
  journal =      j-TCBB,
  volume =       "8",
  number =       "1",
  pages =        "273--281",
  month =        jan,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.136",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:04 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Constructing accurate computational models that
                 explain how ions permeate through a biological ion
                 channel is an important problem in biophysics and drug
                 design. Brownian dynamics simulations are large-scale
                 interacting particle computer simulations for modeling
                 ion channel permeation but can be computationally
                 prohibitive. In this paper, we show the somewhat
                 surprising result that a small-dimensional semi-Markov
                 model can generate events (such as conduction events
                 and dwell times at binding sites in the protein) that
                 are statistically indistinguishable from Brownian
                 dynamics computer simulation. This approach enables the
                 use of extrapolation techniques to predict channel
                 conduction when performing the actual Brownian dynamics
                 simulation that is computationally intractable.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Krishnamurthy:2011:TRL,
  author =       "Vikram Krishnamurthy and Kai-Yiu Luk",
  title =        "2010 {TCBB} Reviewers List",
  journal =      j-TCBB,
  volume =       "8",
  number =       "1",
  pages =        "282--284",
  month =        jan,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.1",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Dec 20 18:39:04 MST 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Sagot:2011:EEb,
  author =       "Marie-France Sagot",
  title =        "{EIC} Editorial",
  journal =      j-TCBB,
  volume =       "8",
  number =       "2",
  pages =        "289--291",
  month =        mar,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.15",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 26 14:16:19 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Wu:2011:GES,
  author =       "Fang-Xiang Wu and Jun Huan",
  title =        "Guest Editorial: Special Focus on Bioinformatics and
                 Systems Biology",
  journal =      j-TCBB,
  volume =       "8",
  number =       "2",
  pages =        "292--293",
  month =        mar,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.16",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 26 14:16:19 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Li:2011:FSF,
  author =       "Yanpeng Li and Xiaohua Hu and Hongfei Lin and Zhiahi
                 Yang",
  title =        "A Framework for Semisupervised Feature Generation and
                 Its Applications in Biomedical Literature Mining",
  journal =      j-TCBB,
  volume =       "8",
  number =       "2",
  pages =        "294--307",
  month =        mar,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.99",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 26 14:16:19 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Feature representation is essential to machine
                 learning and text mining. In this paper, we present a
                 feature coupling generalization (FCG) framework for
                 generating new features from unlabeled data. It selects
                 two special types of features, i.e.,
                 example-distinguishing features (EDFs) and
                 class-distinguishing features (CDFs) from original
                 feature set, and then generalizes EDFs into
                 higher-level features based on their coupling degrees
                 with CDFs in unlabeled data. The advantage is: EDFs
                 with extreme sparsity in labeled data can be enriched
                 by their co-occurrences with CDFs in unlabeled data so
                 that the performance of these low-frequency features
                 can be greatly boosted and new information from
                 unlabeled can be incorporated.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Jeong:2011:PSS,
  author =       "Jong Cheol Jeong and Xiaotong Lin and Xue-wen Chen",
  title =        "On Position-Specific Scoring Matrix for Protein
                 Function Prediction",
  journal =      j-TCBB,
  volume =       "8",
  number =       "2",
  pages =        "308--315",
  month =        mar,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.93",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 26 14:16:19 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "While genome sequencing projects have generated
                 tremendous amounts of protein sequence data for a vast
                 number of genomes, substantial portions of most genomes
                 are still unannotated. Despite the success of
                 experimental methods for identifying protein functions,
                 they are often lab intensive and time consuming. Thus,
                 it is only practical to use in silico methods for the
                 genome-wide functional annotations. In this paper, we
                 propose new features extracted from protein sequence
                 only and machine learning-based methods for
                 computational function prediction. These features are
                 derived from a position-specific scoring matrix, which
                 has shown great potential in other bininformatics
                 problems. We evaluate these features using four
                 different classifiers and yeast protein data.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Oh:2011:ELA,
  author =       "Sangyoon Oh and Min Su Lee and Byoung-Tak Zhang",
  title =        "Ensemble Learning with Active Example Selection for
                 Imbalanced Biomedical Data Classification",
  journal =      j-TCBB,
  volume =       "8",
  number =       "2",
  pages =        "316--325",
  month =        mar,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.96",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 26 14:16:19 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "In biomedical data, the imbalanced data problem occurs
                 frequently and causes poor prediction performance for
                 minority classes. It is because the trained classifiers
                 are mostly derived from the majority class. In this
                 paper, we describe an ensemble learning method combined
                 with active example selection to resolve the imbalanced
                 data problem. Our method consists of three key
                 components: (1) an active example selection algorithm
                 to choose informative examples for training the
                 classifier, (2) an ensemble learning method to combine
                 variations of classifiers derived by active example
                 selection, and (3) an incremental learning scheme to
                 speed up the iterative training procedure for active
                 example selection.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Ibrahim:2011:UQP,
  author =       "Zina Ibrahim and Alioune Ngom and Ahmed Y. Tawfik",
  title =        "Using Qualitative Probability in Reverse-Engineering
                 Gene Regulatory Networks",
  journal =      j-TCBB,
  volume =       "8",
  number =       "2",
  pages =        "326--334",
  month =        mar,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.98",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 26 14:16:19 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "This paper demonstrates the use of qualitative
                 probabilistic networks (QPNs) to aid Dynamic Bayesian
                 Networks (DBNs) in the process of learning the
                 structure of gene regulatory networks from microarray
                 gene expression data. We present a study which shows
                 that QPNs define monotonic relations that are capable
                 of identifying regulatory interactions in a manner that
                 is less susceptible to the many sources of uncertainty
                 that surround gene expression data. Moreover, we
                 construct a model that maps the regulatory interactions
                 of genetic networks to QPN constructs and show its
                 capability in providing a set of candidate regulators
                 for target genes, which is subsequently used to
                 establish a prior structure that the DBN learning
                 algorithm can use and which (1) distinguishes spurious
                 correlations from true regulations, (2) enables the
                 discovery of sets of coregulators of target genes, and
                 (3) results in a more efficient construction of gene
                 regulatory networks.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Sabnis:2011:CTD,
  author =       "Amit Sabnis and Robert W. Harrison",
  title =        "A Continuous-Time, Discrete-State Method for
                 Simulating the Dynamics of Biochemical Systems",
  journal =      j-TCBB,
  volume =       "8",
  number =       "2",
  pages =        "335--341",
  month =        mar,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.97",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 26 14:16:19 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Computational systems biology is largely driven by
                 mathematical modeling and simulation of biochemical
                 networks, via continuous deterministic methods or
                 discrete event stochastic methods. Although the
                 deterministic methods are efficient in predicting the
                 macroscopic behavior of a biochemical system, they are
                 severely limited by their inability to represent the
                 stochastic effects of random molecular fluctuations at
                 lower concentration. In this work, we have presented a
                 novel method for simulating biochemical networks based
                 on a deterministic solution with a modification that
                 permits the incorporation of stochastic effects. To
                 demonstrate the feasibility of our approach, we have
                 tested our method on three previously reported
                 biochemical networks.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Raiford:2011:GOA,
  author =       "Douglas W. Raiford and Dan E. Krane and Travis E. Doom
                 and Michael L. Raymer",
  title =        "A Genetic Optimization Approach for Isolating
                 Translational Efficiency Bias",
  journal =      j-TCBB,
  volume =       "8",
  number =       "2",
  pages =        "342--352",
  month =        mar,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.24",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 26 14:16:19 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The study of codon usage bias is an important research
                 area that contributes to our understanding of molecular
                 evolution, phylogenetic relationships, respiratory
                 lifestyle, and other characteristics. Translational
                 efficiency bias is perhaps the most well-studied codon
                 usage bias, as it is frequently utilized to predict
                 relative protein expression levels. We present a novel
                 approach to isolating translational efficiency bias in
                 microbial genomes. There are several existent methods
                 for isolating translational efficiency bias. Previous
                 approaches are susceptible to the confounding
                 influences of other potentially dominant biases.
                 Additionally, existing approaches to identifying
                 translational efficiency bias generally require both
                 genomic sequence information and prior knowledge of a
                 set of highly expressed genes.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Ram:2011:MBB,
  author =       "Ramesh Ram and Madhu Chetty",
  title =        "A {Markov-Blanket}-Based Model for Gene Regulatory
                 Network Inference",
  journal =      j-TCBB,
  volume =       "8",
  number =       "2",
  pages =        "353--367",
  month =        mar,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.70",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 26 14:16:19 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "An efficient two-step Markov blanket method for
                 modeling and inferring complex regulatory networks from
                 large-scale microarray data sets is presented. The
                 inferred gene regulatory network (GRN) is based on the
                 time series gene expression data capturing the
                 underlying gene interactions. For constructing a highly
                 accurate GRN, the proposed method performs: (1)
                 discovery of a gene's Markov Blanket (MB), (2)
                 formulation of a flexible measure to determine the
                 network's quality, (3) efficient searching with the aid
                 of a guided genetic algorithm, and (4) pruning to
                 obtain a minimal set of correct interactions.
                 Investigations are carried out using both synthetic as
                 well as yeast cell cycle gene expression data sets.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{He:2011:PSC,
  author =       "Zengyou He and Can Yang and Weichuan Yu",
  title =        "A Partial Set Covering Model for Protein Mixture
                 Identification Using Mass Spectrometry Data",
  journal =      j-TCBB,
  volume =       "8",
  number =       "2",
  pages =        "368--380",
  month =        mar,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.54",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 26 14:16:19 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Protein identification is a key and essential step in
                 mass spectrometry (MS) based proteome research. To
                 date, there are many protein identification strategies
                 that employ either MS data or MS/MS data for database
                 searching. While MS-based methods provide wider
                 coverage than MS/MS-based methods, their identification
                 accuracy is lower since MS data have less information
                 than MS/MS data. Thus, it is desired to design more
                 sophisticated algorithms that achieve higher
                 identification accuracy using MS data. Peptide Mass
                 Fingerprinting (PMF) has been widely used to identify
                 single purified proteins from MS data for many years.
                 In this paper, we extend this technology to protein
                 mixture identification.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Wu:2011:ACC,
  author =       "Yonghui Wu and Timothy J. Close and Stefano Lonardi",
  title =        "Accurate Construction of Consensus Genetic Maps via
                 Integer Linear Programming",
  journal =      j-TCBB,
  volume =       "8",
  number =       "2",
  pages =        "381--394",
  month =        mar,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.35",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 26 14:16:19 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "We study the problem of merging genetic maps, when the
                 individual genetic maps are given as directed acyclic
                 graphs. The computational problem is to build a
                 consensus map, which is a directed graph that includes
                 and is consistent with all (or, the vast majority of)
                 the markers in the input maps. However, when markers in
                 the individual maps have ordering conflicts, the
                 resulting consensus map will contain cycles. Here, we
                 formulate the problem of resolving cycles in the
                 context of a parsimonious paradigm that takes into
                 account two types of errors that may be present in the
                 input maps, namely, local reshuffles and global
                 displacements.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Aydin:2011:BMA,
  author =       "Zafer Aydin and Yucel Altunbasak and Hakan Erdogan",
  title =        "{Bayesian} Models and Algorithms for Protein $ \beta
                 $-Sheet Prediction",
  journal =      j-TCBB,
  volume =       "8",
  number =       "2",
  pages =        "395--409",
  month =        mar,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2008.140",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 26 14:16:19 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Prediction of the 3D structure greatly benefits from
                 the information related to secondary structure, solvent
                 accessibility, and nonlocal contacts that stabilize a
                 protein's structure. We address the problem of $ \beta
                 $-sheet prediction defined as the prediction of $ \beta
                 $--strand pairings, interaction types (parallel or
                 antiparallel), and $ \beta $-residue interactions (or
                 contact maps). We introduce a Bayesian approach for
                 proteins with six or less $ \beta $-strands in which we
                 model the conformational features in a probabilistic
                 framework by combining the amino acid pairing
                 potentials with a priori knowledge of $ \beta $-strand
                 organizations. To select the optimum $ \beta $-sheet
                 architecture, we significantly reduce the search space
                 by heuristics that enforce the amino acid pairs with
                 strong interaction potentials.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Cardona:2011:CGT,
  author =       "Gabriel Cardona and Merce Llabres and Francesc
                 Rossello and Gabriel Valiente",
  title =        "Comparison of Galled Trees",
  journal =      j-TCBB,
  volume =       "8",
  number =       "2",
  pages =        "410--427",
  month =        mar,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.60",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 26 14:16:19 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Galled trees, directed acyclic graphs that model
                 evolutionary histories with isolated hybridization
                 events, have become very popular due to both their
                 biological significance and the existence of
                 polynomial-time algorithms for their reconstruction. In
                 this paper, we establish to which extent several
                 distance measures for the comparison of evolutionary
                 networks are metrics for galled trees, and hence, when
                 they can be safely used to evaluate galled tree
                 reconstruction methods.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Leung:2011:DMD,
  author =       "KwongSak Leung and KinHong Lee and JinFeng Wang and
                 Eddie YT Ng and Henry LY Chan and Stephen KW Tsui and
                 Tony SK Mok and Pete Chi-Hang Tse and Joseph JY Sung",
  title =        "Data Mining on {DNA} Sequences of {Hepatitis B}
                 Virus",
  journal =      j-TCBB,
  volume =       "8",
  number =       "2",
  pages =        "428--440",
  month =        mar,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.6",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 26 14:16:19 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Extraction of meaningful information from large
                 experimental data sets is a key element in
                 bioinformatics research. One of the challenges is to
                 identify genomic markers in Hepatitis B Virus (HBV)
                 that are associated with HCC (liver cancer) development
                 by comparing the complete genomic sequences of HBV
                 among patients with HCC and those without HCC. In this
                 study, a data mining framework, which includes
                 molecular evolution analysis, clustering, feature
                 selection, classifier learning, and classification, is
                 introduced. Our research group has collected HBV DNA
                 sequences, either genotype B or C, from over 200
                 patients specifically for this project. In the
                 molecular evolution analysis and clustering, three
                 subgroups have been identified in genotype C and a
                 clustering method has been developed to separate the
                 subgroups.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Lin:2011:DMF,
  author =       "Tien-ho Lin and Robert F. Murphy and Ziv Bar-Joseph",
  title =        "Discriminative Motif Finding for Predicting Protein
                 Subcellular Localization",
  journal =      j-TCBB,
  volume =       "8",
  number =       "2",
  pages =        "441--451",
  month =        mar,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.82",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 26 14:16:19 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Many methods have been described to predict the
                 subcellular location of proteins from sequence
                 information. However, most of these methods either rely
                 on global sequence properties or use a set of known
                 protein targeting motifs to predict protein
                 localization. Here, we develop and test a novel method
                 that identifies potential targeting motifs using a
                 discriminative approach based on hidden Markov models
                 (discriminative HMMs). These models search for motifs
                 that are present in a compartment but absent in other,
                 nearby, compartments by utilizing an hierarchical
                 structure that mimics the protein sorting mechanism. We
                 show that both discriminative motif finding and the
                 hierarchical structure improve localization prediction
                 on a benchmark data set of yeast proteins.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Saraswathi:2011:IPE,
  author =       "Saras Saraswathi and Suresh Sundaram and Narasimhan
                 Sundararajan and Michael Zimmermann and Marit
                 Nilsen-Hamilton",
  title =        "{ICGA-PSO-ELM} Approach for Accurate Multiclass Cancer
                 Classification Resulting in Reduced Gene Sets in Which
                 Genes Encoding Secreted Proteins Are Highly
                 Represented",
  journal =      j-TCBB,
  volume =       "8",
  number =       "2",
  pages =        "452--463",
  month =        mar,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.13",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 26 14:16:19 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "A combination of Integer-Coded Genetic Algorithm
                 (ICGA) and Particle Swarm Optimization (PSO), coupled
                 with the neural-network-based Extreme Learning Machine
                 (ELM), is used for gene selection and cancer
                 classification. ICGA is used with PSO\_ELM to select an
                 optimal set of genes, which is then used to build a
                 classifier to develop an algorithm (ICGA\_PSO\_ELM)
                 that can handle sparse data and sample imbalance. We
                 evaluate the performance of ICGA\_PSO\_ELM and compare
                 our results with existing methods in the literature. An
                 investigation into the functions of the selected genes,
                 using a systems biology approach, revealed that many of
                 the identified genes are involved in cell signaling and
                 proliferation.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Irigoien:2011:MTC,
  author =       "Itziar Irigoien and Sergi Vives and Concepcion
                 Arenas",
  title =        "Microarray Time Course Experiments: Finding Profiles",
  journal =      j-TCBB,
  volume =       "8",
  number =       "2",
  pages =        "464--475",
  month =        mar,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.79",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 26 14:16:19 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Time course studies with microarray techniques and
                 experimental replicates are very useful in biomedical
                 research. We present, in replicate experiments, an
                 alternative approach to select and cluster genes
                 according to a new measure for association between
                 genes. First, the procedure normalizes and standardizes
                 the expression profile of each gene, and then,
                 identifies scaling parameters that will further
                 minimize the distance between replicates of the same
                 gene. Then, the procedure filters out genes with a flat
                 profile, detects differences between replicates, and
                 separates genes without significant differences from
                 the rest. For this last group of genes, we define a
                 mean profile for each gene and use it to compute the
                 distance between two genes.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Dong:2011:NNK,
  author =       "Qiwen Dong and Shuigeng Zhou",
  title =        "Novel Nonlinear Knowledge-Based Mean Force Potentials
                 Based on Machine Learning",
  journal =      j-TCBB,
  volume =       "8",
  number =       "2",
  pages =        "476--486",
  month =        mar,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.86",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 26 14:16:19 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The prediction of 3D structures of proteins from amino
                 acid sequences is one of the most challenging problems
                 in molecular biology. An essential task for solving
                 this problem with coarse-grained models is to deduce
                 effective interaction potentials. The development and
                 evaluation of new energy functions is critical to
                 accurately modeling the properties of biological
                 macromolecules. Knowledge-based mean force potentials
                 are derived from statistical analysis of proteins of
                 known structures. Current knowledge-based potentials
                 are almost in the form of weighted linear sum of
                 interaction pairs. In this study, a class of novel
                 nonlinear knowledge-based mean force potentials is
                 presented. The potential parameters are obtained by
                 nonlinear classifiers, instead of relative frequencies
                 of interaction pairs against a reference state or
                 linear classifiers.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Loriot:2011:CSD,
  author =       "Sebastien Loriot and Sushant Sachdeva and Karine
                 Bastard and Chantal Prevost and Frederic Cazals",
  title =        "On the Characterization and Selection of Diverse
                 Conformational Ensembles with Applications to Flexible
                 Docking",
  journal =      j-TCBB,
  volume =       "8",
  number =       "2",
  pages =        "487--498",
  month =        mar,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.59",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 26 14:16:19 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "To address challenging flexible docking problems, a
                 number of docking algorithms pregenerate large
                 collections of candidate conformers. To remove the
                 redundancy from such ensembles, a central problem in
                 this context is to report a selection of conformers
                 maximizing some geometric diversity criterion. We make
                 three contributions to this problem. First, we resort
                 to geometric optimization so as to report selections
                 maximizing the molecular volume or molecular surface
                 area (MSA) of the selection. Greedy strategies are
                 developed, together with approximation bounds. Second,
                 to assess the efficacy of our algorithms, we
                 investigate two conformer ensembles corresponding to a
                 flexible loop of four protein complexes.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Giegerich:2011:SAS,
  author =       "Robert Giegerich and Christian Hoener zu
                 Siederdissen",
  title =        "Semantics and Ambiguity of Stochastic {RNA} Family
                 Models",
  journal =      j-TCBB,
  volume =       "8",
  number =       "2",
  pages =        "499--516",
  month =        mar,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.12",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 26 14:16:19 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Stochastic models, such as hidden Markov models or
                 stochastic context-free grammars (SCFGs) can fail to
                 return the correct, maximum likelihood solution in the
                 case of semantic ambiguity. This problem arises when
                 the algorithm implementing the model inspects the same
                 solution in different guises. It is a difficult problem
                 in the sense that proving semantic nonambiguity has
                 been shown to be algorithmically undecidable, while
                 compensating for it (by coalescing scores of equivalent
                 solutions) has been shown to be NP-hard. For stochastic
                 context-free grammars modeling RNA secondary structure,
                 it has been shown that the distortion of results can be
                 quite severe. Much less is known about the case when
                 stochastic context-free grammars model the matching of
                 a query sequence to an implicit consensus structure for
                 an RNA family.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Tofigh:2011:SID,
  author =       "Ali Tofigh and Michael Hallett and Jens Lagergren",
  title =        "Simultaneous Identification of Duplications and
                 Lateral Gene Transfers",
  journal =      j-TCBB,
  volume =       "8",
  number =       "2",
  pages =        "517--535",
  month =        mar,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.14",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 26 14:16:19 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The incongruency between a gene tree and a
                 corresponding species tree can be attributed to
                 evolutionary events such as gene duplication and gene
                 loss. This paper describes a combinatorial model where
                 so-called DTL-scenarios are used to explain the
                 differences between a gene tree and a corresponding
                 species tree taking into account gene duplications,
                 gene losses, and lateral gene transfers (also known as
                 horizontal gene transfers). The reasonable biological
                 constraint that a lateral gene transfer may only occur
                 between contemporary species leads to the notion of
                 acyclic DTL-scenarios. Parsimony methods are introduced
                 by defining appropriate optimization problems. We show
                 that finding most parsimonious acyclic DTL-scenarios is
                 NP-hard.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Lieberman:2011:VEA,
  author =       "Michael D. Lieberman and Sima Taheri and whatever Guo
                 and Fatemeh Mirrashed and Inbal Yahav and Aleks Aris
                 and Ben Shneiderman",
  title =        "Visual Exploration across Biomedical Databases",
  journal =      j-TCBB,
  volume =       "8",
  number =       "2",
  pages =        "536--550",
  month =        mar,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.1",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 26 14:16:19 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Though biomedical research often draws on knowledge
                 from a wide variety of fields, few visualization
                 methods for biomedical data incorporate meaningful
                 cross-database exploration. A new approach is offered
                 for visualizing and exploring a query-based subset of
                 multiple heterogeneous biomedical databases. Databases
                 are modeled as an entity-relation graph containing
                 nodes (database records) and links (relationships
                 between records). Users specify a keyword search string
                 to retrieve an initial set of nodes, and then explore
                 intra- and interdatabase links. Results are visualized
                 with user-defined semantic substrates to take advantage
                 of the rich set of attributes usually present in
                 biomedical data.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Hickey:2011:AAN,
  author =       "Glenn Hickey and Mathieu Blanchette and Paz Carmi and
                 Anil Maheshwari and Norbert Zeh",
  title =        "An Approximation Algorithm for the {Noah's Ark
                 Problem} with Random Feature Loss",
  journal =      j-TCBB,
  volume =       "8",
  number =       "2",
  pages =        "551--556",
  month =        mar,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.37",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 26 14:16:19 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The phylogenetic diversity (PD) of a set of species is
                 a measure of their evolutionary distinctness based on a
                 phylogenetic tree. PD is increasingly being adopted as
                 an index of biodiversity in ecological conservation
                 projects. The Noah's Ark Problem (NAP) is an NP-Hard
                 optimization problem that abstracts a fundamental
                 conservation challenge in asking to maximize the
                 expected PD of a set of taxa given a fixed budget,
                 where each taxon is associated with a cost of
                 conservation and a probability of extinction. Only
                 simplified instances of the problem, where one or more
                 parameters are fixed as constants, have as of yet been
                 addressed in the literature.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Cortes:2011:EMM,
  author =       "Juan Cortes and Sophie Barbe and Monique Erard and
                 Thierry Simeon",
  title =        "Encoding Molecular Motions in Voxel Maps",
  journal =      j-TCBB,
  volume =       "8",
  number =       "2",
  pages =        "557--563",
  month =        mar,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.23",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 26 14:16:19 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "This paper builds on the combination of robotic path
                 planning algorithms and molecular modeling methods for
                 computing large-amplitude molecular motions, and
                 introduces voxel maps as a computational tool to encode
                 and to represent such motions. We investigate several
                 applications and show results that illustrate the
                 interest of such representation.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Rocha:2011:GCL,
  author =       "J. Rocha",
  title =        "Graph Comparison by Log-Odds Score Matrices with
                 Application to Protein Topology Analysis",
  journal =      j-TCBB,
  volume =       "8",
  number =       "2",
  pages =        "564--569",
  month =        mar,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.59",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 26 14:16:19 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "A TOPS diagram is a simplified description of the
                 topology of a protein using a graph where nodes are $
                 \alpha $-helices and $ \beta $-strands, and edges
                 correspond to chirality relations and parallel or
                 antiparallel bonds between strands. We present a
                 matching algorithm between two TOPS diagrams where the
                 likelihood of a match is measured according to
                 previously known matches between complete 3D
                 structures. This totally new 3D training is recorded on
                 transition matrices that count the likelihood that a
                 given TOPS feature, or combination thereof, is replaced
                 by another feature on homologs. The new algorithm
                 outperforms existing ones on a benchmark database. Some
                 biologically significant examples are discussed as
                 well.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Fujita:2011:ICR,
  author =       "Andre Fujita and Joao Ricardo Sato and Marcos Almeida
                 Demasi and Rui Yamaguchi and Teppei Shimamura and
                 Carlos Eduardo Ferreira and Mari Cleide Sogayar and
                 Satoru Miyano",
  title =        "Inferring Contagion in Regulatory Networks",
  journal =      j-TCBB,
  volume =       "8",
  number =       "2",
  pages =        "570--576",
  month =        mar,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.40",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Jan 26 14:16:19 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Several gene regulatory network models containing
                 concepts of directionality at the edges have been
                 proposed. However, only a few reports have an
                 interpretable definition of directionality. Here,
                 differently from the standard causality concept defined
                 by Pearl, we introduce the concept of contagion in
                 order to infer directionality at the edges, i.e.,
                 asymmetries in gene expression dependences of
                 regulatory networks. Moreover, we present a bootstrap
                 algorithm in order to test the contagion concept. This
                 technique was applied in simulated data and, also, in
                 an actual large sample of biological data.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Benso:2011:CMG,
  author =       "Alfredo Benso and Stefano {Di Carlo} and Gianfranco
                 Politano",
  title =        "A {cDNA} Microarray Gene Expression Data Classifier
                 for Clinical Diagnostics Based on Graph Theory",
  journal =      j-TCBB,
  volume =       "8",
  number =       "3",
  pages =        "577--591",
  month =        may # "\slash " # jun,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.90",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed May 25 15:41:56 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Yoruk:2011:CSM,
  author =       "Erdem Yoruk and Michael F. Ochs and Donald Geman and
                 Laurent Younes",
  title =        "A Comprehensive Statistical Model for Cell Signaling",
  journal =      j-TCBB,
  volume =       "8",
  number =       "3",
  pages =        "592--606",
  month =        may # "\slash " # jun,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.87",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed May 25 15:41:56 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Wang:2011:FHC,
  author =       "Jianxin Wang and Min Li and Jianer Chen and Yi Pan",
  title =        "A Fast Hierarchical Clustering Algorithm for
                 Functional Modules Discovery in Protein Interaction
                 Networks",
  journal =      j-TCBB,
  volume =       "8",
  number =       "3",
  pages =        "607--620",
  month =        may # "\slash " # jun,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.75",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed May 25 15:41:56 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Feng:2011:MFB,
  author =       "Jianxing Feng and Rui Jiang and Tao Jiang",
  title =        "A Max-Flow-Based Approach to the Identification of
                 Protein Complexes Using Protein Interaction and
                 Microarray Data",
  journal =      j-TCBB,
  volume =       "8",
  number =       "3",
  pages =        "621--634",
  month =        may # "\slash " # jun,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.78",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed May 25 15:41:56 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Huber:2011:PAR,
  author =       "Katharina T. Huber and Leo van Iersel and Steven Kelk
                 and Rados{\l}aw Suchecki",
  title =        "A Practical Algorithm for Reconstructing Level-1
                 Phylogenetic Networks",
  journal =      j-TCBB,
  volume =       "8",
  number =       "3",
  pages =        "635--649",
  month =        may # "\slash " # jun,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.17",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed May 25 15:41:56 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Murphy:2011:TAP,
  author =       "James T. Murphy and Ray Walshe and Marc Devocelle",
  title =        "A Theoretical Analysis of the {Prodrug} Delivery
                 System for Treating Antibiotic-Resistant Bacteria",
  journal =      j-TCBB,
  volume =       "8",
  number =       "3",
  pages =        "650--658",
  month =        may # "\slash " # jun,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.58",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed May 25 15:41:56 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Ghorai:2011:CCG,
  author =       "Santanu Ghorai and Anirban Mukherjee and Sanghamitra
                 Sengupta and Pranab K. Dutta",
  title =        "Cancer Classification from Gene Expression Data by
                 {NPPC} Ensemble",
  journal =      j-TCBB,
  volume =       "8",
  number =       "3",
  pages =        "659--671",
  month =        may # "\slash " # jun,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.36",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed May 25 15:41:56 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Gossler:2011:CBM,
  author =       "Gregor Gossler",
  title =        "Component-Based Modeling and Reachability Analysis of
                 Genetic Networks",
  journal =      j-TCBB,
  volume =       "8",
  number =       "3",
  pages =        "672--682",
  month =        may # "\slash " # jun,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.81",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed May 25 15:41:56 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Tamada:2011:EGW,
  author =       "Yoshinori Tamada and Seiya Imoto and Hiromitsu Araki
                 and Masao Nagasaki and Cristin Print and D. Stephen
                 Charnock-Jones and Satoru Miyano",
  title =        "Estimating Genome-Wide Gene Networks Using
                 Nonparametric {Bayesian} Network Models on Massively
                 Parallel Computers",
  journal =      j-TCBB,
  volume =       "8",
  number =       "3",
  pages =        "683--697",
  month =        may # "\slash " # jun,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.68",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed May 25 15:41:56 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Hudek:2011:FSL,
  author =       "Alexander K. Hudek and Daniel G. Brown",
  title =        "{FEAST}: Sensitive Local Alignment with Multiple Rates
                 of Evolution",
  journal =      j-TCBB,
  volume =       "8",
  number =       "3",
  pages =        "698--709",
  month =        may # "\slash " # jun,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.76",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed May 25 15:41:56 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Allman:2011:ITT,
  author =       "Elizabeth S. Allman and Sonja Petrovi{\'c} and John A.
                 Rhodes and Seth Sullivant",
  title =        "Identifiability of Two-Tree Mixtures for Group-Based
                 Models",
  journal =      j-TCBB,
  volume =       "8",
  number =       "3",
  pages =        "710--722",
  month =        may # "\slash " # jun,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.79",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed May 25 15:41:56 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Yu:2011:INR,
  author =       "Tianwei Yu and Hesen Peng and Wei Sun",
  title =        "Incorporating Nonlinear Relationships in Microarray
                 Missing Value Imputation",
  journal =      j-TCBB,
  volume =       "8",
  number =       "3",
  pages =        "723--731",
  month =        may # "\slash " # jun,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.73",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed May 25 15:41:56 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Song:2011:MSS,
  author =       "Bin Song and {\.I} Esra B{\"u}y{\"u}ktahtakin and
                 Sanjay Ranka and Tamer Kahveci",
  title =        "Manipulating the Steady State of Metabolic Pathways",
  journal =      j-TCBB,
  volume =       "8",
  number =       "3",
  pages =        "732--747",
  month =        may # "\slash " # jun,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.41",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed May 25 15:41:56 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Xu:2011:MLP,
  author =       "Qian Xu and Sinno Jialin Pan and Hannah Hong Xue and
                 Qiang Yang",
  title =        "Multitask Learning for Protein Subcellular Location
                 Prediction",
  journal =      j-TCBB,
  volume =       "8",
  number =       "3",
  pages =        "748--759",
  month =        may # "\slash " # jun,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.22",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed May 25 15:41:56 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Armananzas:2011:PSM,
  author =       "Ruben Armananzas and Yvan Saeys and Inaki Inza and
                 Miguel Garcia-Torres and Concha Bielza and Yves van de
                 Peer and Pedro Larranaga",
  title =        "Peakbin Selection in Mass Spectrometry Data Using a
                 Consensus Approach with Estimation of Distribution
                 Algorithms",
  journal =      j-TCBB,
  volume =       "8",
  number =       "3",
  pages =        "760--774",
  month =        may # "\slash " # jun,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.18",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed May 25 15:41:56 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Mitrofanova:2011:PPF,
  author =       "Antonina Mitrofanova and Vladimir Pavlovic and Bud
                 Mishra",
  title =        "Prediction of Protein Functions with Gene Ontology and
                 Interspecies Protein Homology Data",
  journal =      j-TCBB,
  volume =       "8",
  number =       "3",
  pages =        "775--784",
  month =        may # "\slash " # jun,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.15",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed May 25 15:41:56 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Willson:2011:RNC,
  author =       "Stephen J. Willson",
  title =        "Regular Networks Can be Uniquely Constructed from
                 Their Trees",
  journal =      j-TCBB,
  volume =       "8",
  number =       "3",
  pages =        "785--796",
  month =        may # "\slash " # jun,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.69",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed May 25 15:41:56 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Strutz:2011:SRL,
  author =       "Tilo Strutz",
  title =        "{$3$D} Shape Reconstruction of Loop Objects in {X}-Ray
                 Protein Crystallography",
  journal =      j-TCBB,
  volume =       "8",
  number =       "3",
  pages =        "797--807",
  month =        may # "\slash " # jun,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.67",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed May 25 15:41:56 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Dost:2011:TFM,
  author =       "Banu Dost and Chunlei Wu and Andrew Su and Vineet
                 Bafna",
  title =        "{TCLUST}: {A} Fast Method for Clustering Genome-Scale
                 Expression Data",
  journal =      j-TCBB,
  volume =       "8",
  number =       "3",
  pages =        "808--818",
  month =        may # "\slash " # jun,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.34",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed May 25 15:41:56 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Venkateswaran:2011:TTF,
  author =       "Jayendra Gnanaskandan Venkateswaran and Bin Song and
                 Tamer Kahveci and Christopher Jermaine",
  title =        "{TRIAL}: {A} Tool for Finding Distant Structural
                 Similarities",
  journal =      j-TCBB,
  volume =       "8",
  number =       "3",
  pages =        "819--831",
  month =        may # "\slash " # jun,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.28",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed May 25 15:41:56 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Valentini:2011:TPR,
  author =       "Giorgio Valentini",
  title =        "True Path Rule Hierarchical Ensembles for Genome-Wide
                 Gene Function Prediction",
  journal =      j-TCBB,
  volume =       "8",
  number =       "3",
  pages =        "832--847",
  month =        may # "\slash " # jun,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.38",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed May 25 15:41:56 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Bansal:2011:NFP,
  author =       "Mukul S. Bansal and Ron Shamir",
  title =        "A Note on the Fixed Parameter Tractability of the
                 Gene-Duplication Problem",
  journal =      j-TCBB,
  volume =       "8",
  number =       "3",
  pages =        "848--850",
  month =        may # "\slash " # jun,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.74",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed May 25 15:41:56 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Sehgal:2011:IRD,
  author =       "Aditya Kumar Sehgal and Sanmay Das and Keith Noto and
                 Milton H. {Saier, Jr.} and Charles Elkan",
  title =        "Identifying Relevant Data for a Biological Database:
                 Handcrafted Rules versus Machine Learning",
  journal =      j-TCBB,
  volume =       "8",
  number =       "3",
  pages =        "851--857",
  month =        may # "\slash " # jun,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.83",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed May 25 15:41:56 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Nguyen:2011:TBU,
  author =       "Minh N. Nguyen and Jacek M. Zurada and Jagath C.
                 Rajapakse",
  title =        "Toward Better Understanding of Protein Secondary
                 Structure: Extracting Prediction Rules",
  journal =      j-TCBB,
  volume =       "8",
  number =       "3",
  pages =        "858--864",
  month =        may # "\slash " # jun,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.16",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed May 25 15:41:56 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Borodovsky:2011:GEI,
  author =       "Mark Borodovsky and Teresa M. Przytycka and
                 Sanguthevar Rajasekaran and Alexander Zelikovsky",
  title =        "{Guest Editors}' Introduction to the Special Section
                 on Bioinformatics Research and Applications",
  journal =      j-TCBB,
  volume =       "8",
  number =       "4",
  pages =        "865--866",
  month =        jul,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.92",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 27 10:53:41 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Shibberu:2011:SAP,
  author =       "Yosi Shibberu and Allen Holder",
  title =        "A Spectral Approach to Protein Structure Alignment",
  journal =      j-TCBB,
  volume =       "8",
  number =       "4",
  pages =        "867--875",
  month =        jul,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.24",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 27 10:53:41 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "A new intrinsic geometry based on a spectral analysis
                 is used to motivate methods for aligning protein folds.
                 The geometry is induced by the fact that a distance
                 matrix can be scaled so that its eigenvalues are
                 positive. We provide a mathematically rigorous
                 development of the intrinsic geometry underlying our
                 spectral approach and use it to motivate two alignment
                 algorithms. The first uses eigenvalues alone and
                 dynamic programming to quickly compute a fold
                 alignment. Family identification results are reported
                 for the Skolnick40 and Proteus300 data sets. The second
                 algorithm extends our spectral method by iterating
                 between our intrinsic geometry and the 3D geometry of a
                 fold to make high-quality alignments.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Ferraro:2011:ACQ,
  author =       "Nicola Ferraro and Luigi Palopoli and Simona Panni and
                 Simona E. Rombo",
  title =        "Asymmetric Comparison and Querying of Biological
                 Networks",
  journal =      j-TCBB,
  volume =       "8",
  number =       "4",
  pages =        "876--889",
  month =        jul,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.29",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 27 10:53:41 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Comparing and querying the protein-protein interaction
                 (PPI) networks of different organisms is important to
                 infer knowledge about conservation across species.
                 Known methods that perform these tasks operate
                 symmetrically, i.e., they do not assign a distinct role
                 to the input PPI networks. However, in most cases, the
                 input networks are indeed distinguishable on the basis
                 of how the corresponding organism is biologically well
                 characterized. In this paper a new idea is developed,
                 that is, to exploit differences in the characterization
                 of organisms at hand in order to devise methods for
                 comparing their PPI networks. We use the PPI network
                 (called Master) of the best characterized organism as a
                 fingerprint to guide the alignment process to the
                 second input network (called Slave), so that generated
                 results preferably retain the structural
                 characteristics of the Master network.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Wiedenhoeft:2011:PMI,
  author =       "John Wiedenhoeft and Roland Krause and Oliver
                 Eulenstein",
  title =        "The Plexus Model for the Inference of Ancestral
                 Multidomain Proteins",
  journal =      j-TCBB,
  volume =       "8",
  number =       "4",
  pages =        "890--901",
  month =        jul,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.22",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 27 10:53:41 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Interactions of protein domains control essential
                 cellular processes. Thus, inferring the evolutionary
                 histories of multidomain proteins in the context of
                 their families can provide rewarding insights into
                 protein function. However, methods to infer these
                 histories are challenged by the complexity of
                 macroevolutionary events. Here, we address this
                 challenge by describing an algorithm that computes a
                 novel network-like structure, called plexus, which
                 represents the evolution of domains and their
                 combinations. Finally, we demonstrate the performance
                 of this algorithm with empirical data sets.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Pattengale:2011:UHP,
  author =       "Nicholas Pattengale and Andre Aberer and Krister
                 Swenson and Alexandros Stamatakis and Bernard Moret",
  title =        "Uncovering Hidden Phylogenetic Consensus in Large Data
                 Sets",
  journal =      j-TCBB,
  volume =       "8",
  number =       "4",
  pages =        "902--911",
  month =        jul,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.28",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 27 10:53:41 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Many of the steps in phylogenetic reconstruction can
                 be confounded by ``rogue'' taxa---taxa that cannot be
                 placed with assurance anywhere within the tree, indeed,
                 whose location within the tree varies with almost any
                 choice of algorithm or parameters. Phylogenetic
                 consensus methods, in particular, are known to suffer
                 from this problem. In this paper, we provide a novel
                 framework to define and identify rogue taxa. In this
                 framework, we formulate a bicriterion optimization
                 problem, the relative information criterion, that
                 models the net increase in useful information present
                 in the consensus tree when certain taxa are removed
                 from the input data. We also provide an effective
                 greedy heuristic to identify a subset of rogue taxa and
                 use this heuristic in a series of experiments, with
                 both pathological examples from the literature and a
                 collection of large biological data sets.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Gysel:2011:EIC,
  author =       "Rob Gysel and Daniel Gusfield",
  title =        "Extensions and Improvements to the Chordal Graph
                 Approach to the Multistate Perfect Phylogeny Problem",
  journal =      j-TCBB,
  volume =       "8",
  number =       "4",
  pages =        "912--917",
  month =        jul,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.27",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 27 10:53:41 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The multistate perfect phylogeny problem is a classic
                 problem in computational biology. When no perfect
                 phylogeny exists, it is of interest to find a set of
                 characters to remove in order to obtain a perfect
                 phylogeny in the remaining data. This is known as the
                 character removal problem. We show how to use chordal
                 graphs and triangulations to solve the character
                 removal problem for an arbitrary number of states,
                 which was previously unsolved. We outline a
                 preprocessing technique that speeds up the computation
                 of the minimal separators of a graph.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Tsai:2011:CTA,
  author =       "Ming-Chi Tsai and Guy E. Blelloch and R. Ravi and
                 Russell Schwartz",
  title =        "A Consensus Tree Approach for Reconstructing Human
                 Evolutionary History and Detecting Population
                 Substructure",
  journal =      j-TCBB,
  volume =       "8",
  number =       "4",
  pages =        "918--928",
  month =        jul,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.23",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 27 10:53:41 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The random accumulation of variations in the human
                 genome over time implicitly encodes a history of how
                 human populations have arisen, dispersed, and
                 intermixed since we emerged as a species.
                 Reconstructing that history is a challenging
                 computational and statistical problem but has important
                 applications both to basic research and to the
                 discovery of genotype-phenotype correlations. We
                 present a novel approach to inferring human
                 evolutionary history from genetic variation data. We
                 use the idea of consensus trees, a technique generally
                 used to reconcile species trees from divergent gene
                 trees, adapting it to the problem of finding robust
                 relationships within a set of intraspecies phylogenies
                 derived from local regions of the genome.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Bandyopadhyay:2011:BIM,
  author =       "Sanghamitra Bandyopadhyay and Malay Bhattacharyya",
  title =        "A Biologically Inspired Measure for Coexpression
                 Analysis",
  journal =      j-TCBB,
  volume =       "8",
  number =       "4",
  pages =        "929--942",
  month =        jul,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.106",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 27 10:53:41 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Two genes are said to be coexpressed if their
                 expression levels have a similar spatial or temporal
                 pattern. Ever since the profiling of gene microarrays
                 has been in progress, computational modeling of
                 coexpression has acquired a major focus. As a result,
                 several similarity/distance measures have evolved over
                 time to quantify coexpression similarity/dissimilarity
                 between gene pairs. Of these, correlation coefficient
                 has been established to be a suitable quantifier of
                 pairwise coexpression. In general, correlation
                 coefficient is good for symbolizing linear dependence,
                 but not for nonlinear dependence. In spite of this
                 drawback, it outperforms many other existing measures
                 in modeling the dependency in biological data.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Tenazinha:2011:SMM,
  author =       "Nuno Tenazinha and Susana Vinga",
  title =        "A Survey on Methods for Modeling and Analyzing
                 Integrated Biological Networks",
  journal =      j-TCBB,
  volume =       "8",
  number =       "4",
  pages =        "943--958",
  month =        jul,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.117",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 27 10:53:41 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Understanding how cellular systems build up integrated
                 responses to their dynamically changing environment is
                 one of the open questions in Systems Biology. Despite
                 their intertwinement, signaling networks, gene
                 regulation and metabolism have been frequently modeled
                 independently in the context of well-defined
                 subsystems. For this purpose, several mathematical
                 formalisms have been developed according to the
                 features of each particular network under study.
                 Nonetheless, a deeper understanding of cellular
                 behavior requires the integration of these various
                 systems into a model capable of capturing how they
                 operate as an ensemble. With the recent advances in the
                 ``omics'' technologies, more data is becoming available
                 and, thus, recent efforts have been driven toward this
                 integrated modeling approach.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Huang:2011:IHA,
  author =       "Chao-Wen Huang and Wun-Shiun Lee and Sun-Yuan Hsieh",
  title =        "An Improved Heuristic Algorithm for Finding Motif
                 Signals in {DNA} Sequences",
  journal =      j-TCBB,
  volume =       "8",
  number =       "4",
  pages =        "959--975",
  month =        jul,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.92",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 27 10:53:41 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The planted (l,d)-motif search problem is a
                 mathematical abstraction of the DNA functional site
                 discovery task. In this paper, we propose a heuristic
                 algorithm that can find planted (l,d)-signals in a
                 given set of DNA sequences. Evaluations on simulated
                 data sets demonstrate that the proposed algorithm
                 outperforms current widely used motif finding
                 algorithms. We also report the results of experiments
                 on real biological data sets.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Bocker:2011:DGS,
  author =       "Sebastian Bocker and Birte Kehr and Florian Rasche",
  title =        "Determination of Glycan Structure from Tandem Mass
                 Spectra",
  journal =      j-TCBB,
  volume =       "8",
  number =       "4",
  pages =        "976--986",
  month =        jul,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.129",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 27 10:53:41 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Glycans are molecules made from simple sugars that
                 form complex tree structures. Glycans constitute one of
                 the most important protein modifications and
                 identification of glycans remains a pressing problem in
                 biology. Unfortunately, the structure of glycans is
                 hard to predict from the genome sequence of an
                 organism. In this paper, we consider the problem of
                 deriving the topology of a glycan solely from tandem
                 mass spectrometry (MS) data. We study, how to generate
                 glycan tree candidates that sufficiently match the
                 sample mass spectrum, avoiding the combinatorial
                 explosion of glycan structures. Unfortunately, the
                 resulting problem is known to be computationally hard.
                 We present an efficient exact algorithm for this
                 problem based on fixed-parameter algorithmics that can
                 process a spectrum in a matter of seconds.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Wong:2011:EAE,
  author =       "Samuel S. Y. Wong and Weimin Luo and Keith C. C.
                 Chan",
  title =        "{EvoMD}: An Algorithm for Evolutionary Molecular
                 Design",
  journal =      j-TCBB,
  volume =       "8",
  number =       "4",
  pages =        "987--1003",
  month =        jul,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.100",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 27 10:53:41 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Traditionally, Computer-Aided Molecular Design (CAMD)
                 uses heuristic search and mathematical programming to
                 tackle the molecular design problem. But these
                 techniques do not handle large and nonlinear search
                 space very well. To overcome these drawbacks,
                 graph-based evolutionary algorithms (EAs) have been
                 proposed to evolve molecular design by mimicking
                 chemical reactions on the exchange of chemical bonds
                 and components between molecules. For these EAs to
                 perform their tasks, known molecular components, which
                 can serve as building blocks for the molecules to be
                 designed, and known chemical rules, which govern
                 chemical combination between different components, have
                 to be introduced before the evolutionary process can
                 take place.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Merelli:2011:IBS,
  author =       "Ivan Merelli and Paolo Cozzi and Daniele D'Agostino
                 and Andrea Clematis and Luciano Milanesi",
  title =        "Image-Based Surface Matching Algorithm Oriented to
                 Structural Biology",
  journal =      j-TCBB,
  volume =       "8",
  number =       "4",
  pages =        "1004--1016",
  month =        jul,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.21",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 27 10:53:41 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Emerging technologies for structure matching based on
                 surface descriptions have demonstrated their
                 effectiveness in many research fields. In particular,
                 they can be successfully applied to in silico studies
                 of structural biology. Protein activities, in fact, are
                 related to the external characteristics of these
                 macromolecules and the ability to match surfaces can be
                 important to infer information about their possible
                 functions and interactions. In this work, we present a
                 surface-matching algorithm, based on encoding the outer
                 morphology of proteins in images of local description,
                 which allows us to establish point-to-point
                 correlations among macromolecular surfaces using
                 image-processing functions. Discarding methods relying
                 on biological analysis of atomic structures and
                 expensive computational approaches based on energetic
                 studies, this algorithm can successfully be used for
                 macromolecular recognition by employing local surface
                 features.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{DiLena:2011:TOS,
  author =       "Pietro {Di Lena} and Piero Fariselli and Luciano
                 Margara and Marco Vassura and Rita Casadio",
  title =        "Is There an Optimal Substitution Matrix for Contact
                 Prediction with Correlated Mutations?",
  journal =      j-TCBB,
  volume =       "8",
  number =       "4",
  pages =        "1017--1028",
  month =        jul,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.91",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 27 10:53:41 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Correlated mutations in proteins are believed to occur
                 in order to preserve the protein functional folding
                 through evolution. Their values can be deduced from
                 sequence and/or structural alignments and are
                 indicative of residue contacts in the protein
                 three-dimensional structure. A correlation among pairs
                 of residues is routinely evaluated with the Pearson
                 correlation coefficient and the MCLACHLAN similarity
                 matrix. In literature, there is no justification for
                 the adoption of the MCLACHLAN instead of other
                 substitution matrices. In this paper, we approach the
                 problem of computing the optimal similarity matrix for
                 contact prediction with correlated mutations, i.e., the
                 similarity matrix that maximizes the accuracy of
                 contact prediction with correlated mutations.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Huber:2011:MMT,
  author =       "Katharina T. Huber and Andreas Spillner and Rados law
                 Suchecki and Vincent Moulton",
  title =        "Metrics on Multilabeled Trees: Interrelationships and
                 Diameter Bounds",
  journal =      j-TCBB,
  volume =       "8",
  number =       "4",
  pages =        "1029--1040",
  month =        jul,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.122",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 27 10:53:41 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Multilabeled trees or MUL-trees, for short, are trees
                 whose leaves are labeled by elements of some nonempty
                 finite set X such that more than one leaf may be
                 labeled by the same element of X. This class of trees
                 includes phylogenetic trees and tree shapes. MUL-trees
                 arise naturally in, for example, biogeography and gene
                 evolution studies and also in the area of phylogenetic
                 network reconstruction. In this paper, we introduce
                 novel metrics which may be used to compare MUL-trees,
                 most of which generalize well-known metrics on
                 phylogenetic trees and tree shapes. These metrics can
                 be used, for example, to better understand the space of
                 MUL-trees or to help visualize collections of
                 MUL-trees.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Zhao:2011:MKI,
  author =       "Xin Zhao and Leo Wang-Kit Cheung",
  title =        "Multiclass Kernel-Imbedded {Gaussian} Processes for
                 Microarray Data Analysis",
  journal =      j-TCBB,
  volume =       "8",
  number =       "4",
  pages =        "1041--1053",
  month =        jul,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.85",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 27 10:53:41 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Identifying significant differentially expressed genes
                 of a disease can help understand the disease at the
                 genomic level. A hierarchical statistical model named
                 multiclass kernel-imbedded Gaussian process (mKIGP) is
                 developed under a Bayesian framework for a multiclass
                 classification problem using microarray gene expression
                 data. Specifically, based on a multinomial probit
                 regression setting, an empirically adaptive algorithm
                 with a cascading structure is designed to find
                 appropriate featuring kernels, to discover potentially
                 significant genes, and to make optimal tumor/cancer
                 class predictions. A Gibbs sampler is adopted as the
                 core of the algorithm to perform Bayesian inferences. A
                 prescreening procedure is implemented to alleviate the
                 computational complexity.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Zhang:2011:PTN,
  author =       "Peng Zhang and Houqiang Li and Honghui Wang and Wong
                 Stephen and Xiaobo Zhou",
  title =        "Peak Tree: {A} New Tool for Multiscale Hierarchical
                 Representation and Peak Detection of Mass Spectrometry
                 Data",
  journal =      j-TCBB,
  volume =       "8",
  number =       "4",
  pages =        "1054--1066",
  month =        jul,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.56",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 27 10:53:41 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Peak detection is one of the most important steps in
                 mass spectrometry (MS) analysis. However, the detection
                 result is greatly affected by severe spectrum
                 variations. Unfortunately, most current peak detection
                 methods are neither flexible enough to revise false
                 detection results nor robust enough to resist spectrum
                 variations. To improve flexibility, we introduce peak
                 tree to represent the peak information in MS spectra.
                 Each tree node is a peak judgment on a range of scales,
                 and each tree decomposition, as a set of nodes, is a
                 candidate peak detection result. To improve robustness,
                 we combine peak detection and common peak alignment
                 into a closed-loop framework, which finds the optimal
                 decomposition via both peak intensity and common peak
                 information.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{El-Manzalawy:2011:PMI,
  author =       "Yasser El-Manzalawy and Drena Dobbs and Vasant
                 Honavar",
  title =        "Predicting {MHC-II} Binding Affinity Using Multiple
                 Instance Regression",
  journal =      j-TCBB,
  volume =       "8",
  number =       "4",
  pages =        "1067--1079",
  month =        jul,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.94",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 27 10:53:41 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Reliably predicting the ability of antigen peptides to
                 bind to major histocompatibility complex class II
                 (MHC-II) molecules is an essential step in developing
                 new vaccines. Uncovering the amino acid sequence
                 correlates of the binding affinity of MHC-II binding
                 peptides is important for understanding pathogenesis
                 and immune response. The task of predicting MHC-II
                 binding peptides is complicated by the significant
                 variability in their length. Most existing
                 computational methods for predicting MHC-II binding
                 peptides focus on identifying a nine amino acids core
                 region in each binding peptide. We formulate the
                 problems of qualitatively and quantitatively predicting
                 flexible length MHC-II peptides as multiple instance
                 learning and multiple instance regression problems,
                 respectively. Based on this formulation, we introduce
                 MHCMIR, a novel method for predicting MHC-II binding
                 affinity using multiple instance regression. We present
                 results of experiments using several benchmark data
                 sets that show that MHCMIR is competitive with the
                 state-of-the-art methods for predicting MHC-II binding
                 peptides. An online web server that implements the
                 MHCMIR method for MHC-II binding affinity prediction is
                 freely accessible at
                 \path=http://ailab.cs.iastate.edu/mhcmir/=.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Yang:2011:RFS,
  author =       "Feng Yang and K. Z. Mao",
  title =        "Robust Feature Selection for Microarray Data Based on
                 Multicriterion Fusion",
  journal =      j-TCBB,
  volume =       "8",
  number =       "4",
  pages =        "1080--1092",
  month =        jul,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.103",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 27 10:53:41 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Feature selection often aims to select a compact
                 feature subset to build a pattern classifier with
                 reduced complexity, so as to achieve improved
                 classification performance. From the perspective of
                 pattern analysis, producing stable or robust solution
                 is also a desired property of a feature selection
                 algorithm. However, the issue of robustness is often
                 overlooked in feature selection. In this study, we
                 analyze the robustness issue existing in feature
                 selection for high-dimensional and small-sized
                 gene-expression data, and propose to improve robustness
                 of feature selection algorithm by using multiple
                 feature selection evaluation criteria. Based on this
                 idea, a multicriterion fusion-based recursive feature
                 elimination (MCF-RFE) algorithm is developed with the
                 goal of improving both classification performance and
                 stability of feature selection results.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Tino:2011:SCG,
  author =       "Peter Ti{\v{n}}o and Hongya Zhao and Hong Yan",
  title =        "Searching for Coexpressed Genes in Three-Color {cDNA}
                 Microarray Data Using a Probabilistic Model-Based
                 {Hough Transform}",
  journal =      j-TCBB,
  volume =       "8",
  number =       "4",
  pages =        "1093--1107",
  month =        jul,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.120",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 27 10:53:41 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "The effects of a drug on the genomic scale can be
                 assessed in a three-color cDNA microarray with the
                 three color intensities represented through the
                 so-called hexaMplot. In our recent study, we have shown
                 that the Hough Transform (HT) applied to the hexaMplot
                 can be used to detect groups of coexpressed genes in
                 the normal-disease-drug samples. However, the standard
                 HT is not well suited for the purpose because (1) the
                 assayed genes need first to be hard-partitioned into
                 equally and differentially expressed genes, with HT
                 ignoring possible information in the former group; (2)
                 the hexaMplot coordinates are negatively correlated and
                 there is no direct way of expressing this in the
                 standard HT and (3) it is not clear how to quantify the
                 association of coexpressed genes with the line along
                 which they cluster.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Wang:2011:IMP,
  author =       "Li-San Wang and Jim Leebens-Mack and P. Kerr Wall and
                 Kevin Beckmann and Claude W. dePamphilis and Tandy
                 Warnow",
  title =        "The Impact of Multiple Protein Sequence Alignment on
                 Phylogenetic Estimation",
  journal =      j-TCBB,
  volume =       "8",
  number =       "4",
  pages =        "1108--1119",
  month =        jul,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2009.68",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 27 10:53:41 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Multiple sequence alignment is typically the first
                 step in estimating phylogenetic trees, with the
                 assumption being that as alignments improve, so will
                 phylogenetic reconstructions. Over the last decade or
                 so, new multiple sequence alignment methods have been
                 developed to improve comparative analyses of protein
                 structure, but these new methods have not been
                 typically used in phylogenetic analyses. In this paper,
                 we report on a simulation study that we performed to
                 evaluate the consequences of using these new multiple
                 sequence alignment methods in terms of the resultant
                 phylogenetic reconstruction. We find that while
                 alignment accuracy is positively correlated with
                 phylogenetic accuracy, the amount of improvement in
                 phylogenetic estimation that results from an improved
                 alignment can range from quite small to substantial.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Sadjad:2011:TRS,
  author =       "Bashir Sadjad and Zsolt Zsoldos",
  title =        "Toward a Robust Search Method for the Protein--Drug
                 Docking Problem",
  journal =      j-TCBB,
  volume =       "8",
  number =       "4",
  pages =        "1120--1133",
  month =        jul,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.70",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 27 10:53:41 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Predicting the binding mode(s) of a drug molecule to a
                 target receptor is pivotal in structure-based rational
                 drug design. In contrast to most approaches to solve
                 this problem, the idea in this paper is to analyze the
                 search problem from a computational perspective. By
                 building on top of an existing docking tool, new
                 methods are proposed and relevant computational results
                 are proven. These methods and results are applicable
                 for other place-and-join frameworks as well. A fast
                 approximation scheme for the docking of rigid fragments
                 is described that guarantees certain geometric
                 approximation factors. It is also demonstrated that
                 this can be translated into an energy approximation for
                 simple scoring functions.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Chen:2011:ARD,
  author =       "Yang Chen and Jinglu Hu",
  title =        "Accurate Reconstruction for {DNA} Sequencing by
                 Hybridization Based on a Constructive Heuristic",
  journal =      j-TCBB,
  volume =       "8",
  number =       "4",
  pages =        "1134--1140",
  month =        jul,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.89",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 27 10:53:41 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Sequencing by hybridization is a promising
                 cost-effective technology for high-throughput DNA
                 sequencing via microarray chips. However, due to the
                 effects of spectrum errors rooted in experimental
                 conditions, an accurate and fast reconstruction of
                 original sequences has become a challenging problem. In
                 the last decade, a variety of analyses and designs have
                 been tried to overcome this problem, where different
                 strategies have different trade-offs in speed and
                 accuracy. Motivated by the idea that the errors could
                 be identified by analyzing the interrelation of
                 spectrum elements, this paper presents a constructive
                 heuristic algorithm, featuring an accurate
                 reconstruction guided by a set of well-defined criteria
                 and rules.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Guillemot:2011:CSM,
  author =       "Sylvain Guillemot and Jesper Jansson and Wing-Kin
                 Sung",
  title =        "Computing a Smallest Multilabeled Phylogenetic Tree
                 from Rooted Triplets",
  journal =      j-TCBB,
  volume =       "8",
  number =       "4",
  pages =        "1141--1147",
  month =        jul,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.77",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 27 10:53:41 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Peters:2011:TSC,
  author =       "Tim Peters and David W. Bulger and To-ha Loi and Jean
                 Yee Hwa Yang and David Ma",
  title =        "Two-Step Cross-Entropy Feature Selection for
                 Microarrays --- Power Through Complementarity",
  journal =      j-TCBB,
  volume =       "8",
  number =       "4",
  pages =        "1148--1151",
  month =        jul,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.30",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Mon Jun 27 10:53:41 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Current feature selection methods for supervised
                 classification of tissue samples from microarray data
                 generally fail to exploit complementary discriminatory
                 power that can be found in sets of features [CHECK END
                 OF SENTENCE]. Using a feature selection method with the
                 computational architecture of the cross-entropy method
                 [CHECK END OF SENTENCE], including an additional
                 preliminary step ensuring a lower bound on the number
                 of times any feature is considered, we show when
                 testing on a human lymph node data set that there are a
                 significant number of genes that perform well when
                 their complementary power is assessed, but ``pass under
                 the radar'' of popular feature selection methods that
                 only assess genes individually on a given
                 classification tool.",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Zhu:2011:GMA,
  author =       "Dongxiao Zhu and Lipi Acharya and Hui Zhang",
  title =        "A Generalized Multivariate Approach to Pattern
                 Discovery from Replicated and Incomplete Genome-Wide
                 Measurements",
  journal =      j-TCBB,
  volume =       "8",
  number =       "5",
  pages =        "1153--1169",
  month =        sep,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.102",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Aug 17 09:10:05 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Chang:2011:NKD,
  author =       "Rui Chang and Robert Shoemaker and Wei Wang",
  title =        "A Novel Knowledge-Driven Systems Biology Approach for
                 Phenotype Prediction upon Genetic Intervention",
  journal =      j-TCBB,
  volume =       "8",
  number =       "5",
  pages =        "1170--1182",
  month =        sep,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.18",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Aug 17 09:10:05 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Irurozki:2011:PPH,
  author =       "Ekhine Irurozki and Borja Calvo and Jose A. Lozano",
  title =        "A Preprocessing Procedure for Haplotype Inference by
                 Pure Parsimony",
  journal =      j-TCBB,
  volume =       "8",
  number =       "5",
  pages =        "1183--1195",
  month =        sep,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.125",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Aug 17 09:10:05 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Battagliero:2011:EAA,
  author =       "Simone Battagliero and Giuseppe Puglia and Saverio
                 Vicario and Francesco Rubino and Gaetano Scioscia and
                 Pietro Leo",
  title =        "An Efficient Algorithm for Approximating Geodesic
                 Distances in Tree Space",
  journal =      j-TCBB,
  volume =       "8",
  number =       "5",
  pages =        "1196--1207",
  month =        sep,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.121",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Aug 17 09:10:05 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{John:2011:CCP,
  author =       "David J. John and Jacquelyn S. Fetrow and James L.
                 Norris",
  title =        "Continuous Cotemporal Probabilistic Modeling of
                 Systems Biology Networks from Sparse Data",
  journal =      j-TCBB,
  volume =       "8",
  number =       "5",
  pages =        "1208--1222",
  month =        sep,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.95",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Aug 17 09:10:05 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Guziolowski:2011:DLR,
  author =       "Carito Guziolowski and Sylvain Blachon and Tatiana
                 Baumuratova and Gautier Stoll and Ovidiu Radulescu and
                 Anne Siegel",
  title =        "Designing Logical Rules to Model the Response of
                 Biomolecular Networks with Complex Interactions: An
                 Application to Cancer Modeling",
  journal =      j-TCBB,
  volume =       "8",
  number =       "5",
  pages =        "1223--1234",
  month =        sep,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.71",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Aug 17 09:10:05 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Sahu:2011:ELH,
  author =       "Sitanshu Sekhar Sahu and Ganapati Panda",
  title =        "Efficient Localization of Hot Spots in Proteins Using
                 a Novel {$S$}-Transform Based Filtering Approach",
  journal =      j-TCBB,
  volume =       "8",
  number =       "5",
  pages =        "1235--1246",
  month =        sep,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.109",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Aug 17 09:10:05 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Flores:2011:FFM,
  author =       "Samuel Coulbourn Flores and Michael Sherman and
                 Christopher M. Bruns and Peter Eastman and Russ B.
                 Altman",
  title =        "Fast Flexible Modeling of {RNA} Structure Using
                 Internal Coordinates",
  journal =      j-TCBB,
  volume =       "8",
  number =       "5",
  pages =        "1247--1257",
  month =        sep,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.104",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Aug 17 09:10:05 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Wang:2011:IAF,
  author =       "Biing-Feng Wang and Chien-Hsin Lin",
  title =        "Improved Algorithms for Finding Gene Teams and
                 Constructing Gene Team Trees",
  journal =      j-TCBB,
  volume =       "8",
  number =       "5",
  pages =        "1258--1272",
  month =        sep,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.127",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Aug 17 09:10:05 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Zheng:2011:MBS,
  author =       "Chun-Hou Zheng and Lei Zhang and To-Yee Ng and Chi
                 Keung Shiu and De-Shuang Huang",
  title =        "Metasample-Based Sparse Representation for Tumor
                 Classification",
  journal =      j-TCBB,
  volume =       "8",
  number =       "5",
  pages =        "1273--1282",
  month =        sep,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.20",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Aug 17 09:10:05 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Peng:2011:MSA,
  author =       "Qian Peng and Andrew D. Smith",
  title =        "Multiple Sequence Assembly from Reads Alignable to a
                 Common Reference Genome",
  journal =      j-TCBB,
  volume =       "8",
  number =       "5",
  pages =        "1283--1295",
  month =        sep,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.107",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Aug 17 09:10:05 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Betzler:2011:PAF,
  author =       "Nadja Betzler and Rene van Bevern and Michael R.
                 Fellows and Christian Komusiewicz and Rolf
                 Niedermeier",
  title =        "Parameterized Algorithmics for Finding Connected
                 Motifs in Biological Networks",
  journal =      j-TCBB,
  volume =       "8",
  number =       "5",
  pages =        "1296--1308",
  month =        sep,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.19",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Aug 17 09:10:05 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Kim:2011:PMS,
  author =       "Jong Kyoung Kim and Seungjin Choi",
  title =        "Probabilistic Models for Semisupervised Discriminative
                 Motif Discovery in {DNA} Sequences",
  journal =      j-TCBB,
  volume =       "8",
  number =       "5",
  pages =        "1309--1317",
  month =        sep,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.84",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Aug 17 09:10:05 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Feijao:2011:SBL,
  author =       "Pedro Feijao and Joao Meidanis",
  title =        "{SCJ}: {A} Breakpoint-Like Distance that Simplifies
                 Several Rearrangement Problems",
  journal =      j-TCBB,
  volume =       "8",
  number =       "5",
  pages =        "1318--1329",
  month =        sep,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.34",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Aug 17 09:10:05 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Mernberger:2011:SSG,
  author =       "Marco Mernberger and Gerhard Klebe and Eyke
                 Hullermeier",
  title =        "{SEGA}: Semiglobal Graph Alignment for Structure-Based
                 Protein Comparison",
  journal =      j-TCBB,
  volume =       "8",
  number =       "5",
  pages =        "1330--1343",
  month =        sep,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.35",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Aug 17 09:10:05 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Boyen:2011:SGM,
  author =       "Peter Boyen and Dries {Van Dyck} and Frank Neven and
                 Roeland C. H. J. van Ham and Aalt D. J. van Dijk",
  title =        "{SLIDER}: {A} Generic Metaheuristic for the Discovery
                 of Correlated Motifs in Protein-Protein Interaction
                 Networks",
  journal =      j-TCBB,
  volume =       "8",
  number =       "5",
  pages =        "1344--1357",
  month =        sep,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.17",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Aug 17 09:10:05 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Buhrman:2011:SMR,
  author =       "Harry Buhrman and Peter T. S. van der Gulik and Steven
                 M. Kelk and Wouter M. Koolen and Leen Stougie",
  title =        "Some Mathematical Refinements Concerning Error
                 Minimization in the Genetic Code",
  journal =      j-TCBB,
  volume =       "8",
  number =       "5",
  pages =        "1358--1372",
  month =        sep,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.40",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Aug 17 09:10:05 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Wong:2011:UKA,
  author =       "William W. L. Wong and Forbes J. Burkowski",
  title =        "Using Kernel Alignment to Select Features of Molecular
                 Descriptors in a {QSAR} Study",
  journal =      j-TCBB,
  volume =       "8",
  number =       "5",
  pages =        "1373--1384",
  month =        sep,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.31",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Aug 17 09:10:05 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Muselli:2011:MMV,
  author =       "Marco Muselli and Alberto Bertoni and Marco Frasca and
                 Alessandro Beghini and Francesca Ruffino and Giorgio
                 Valentini",
  title =        "A Mathematical Model for the Validation of Gene
                 Selection Methods",
  journal =      j-TCBB,
  volume =       "8",
  number =       "5",
  pages =        "1385--1392",
  month =        sep,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.83",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Aug 17 09:10:05 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Dubrova:2011:SBA,
  author =       "Elena Dubrova and Maxim Teslenko",
  title =        "A {SAT}-Based Algorithm for Finding Attractors in
                 Synchronous {Boolean} Networks",
  journal =      j-TCBB,
  volume =       "8",
  number =       "5",
  pages =        "1393--1399",
  month =        sep,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.20",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Aug 17 09:10:05 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Chen:2011:FEA,
  author =       "Zhi-Zhong Chen and Lusheng Wang",
  title =        "Fast Exact Algorithms for the Closest String and
                 Substring Problems with Application to the Planted {$
                 (L, d) $}-Motif Model",
  journal =      j-TCBB,
  volume =       "8",
  number =       "5",
  pages =        "1400--1410",
  month =        sep,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.21",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Aug 17 09:10:05 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Grusea:2011:DNC,
  author =       "Simona Grusea",
  title =        "On the Distribution of the Number of Cycles in the
                 Breakpoint Graph of a Random Signed Permutation",
  journal =      j-TCBB,
  volume =       "8",
  number =       "5",
  pages =        "1411--1416",
  month =        sep,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.123",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Aug 17 09:10:05 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Befekadu:2011:PMR,
  author =       "Getachew K. Befekadu and Mahlet G. Tadesse and
                 Tsung-Heng Tsai and Habtom W. Ressom",
  title =        "Probabilistic Mixture Regression Models for Alignment
                 of {LC-MS} Data",
  journal =      j-TCBB,
  volume =       "8",
  number =       "5",
  pages =        "1417--1424",
  month =        sep,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.88",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Aug 17 09:10:05 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Magni:2011:SPI,
  author =       "Paolo Magni and Angela Simeone and Sandra Healy and
                 Antonella Isacchi and Roberta Bosotti",
  title =        "Summarizing Probe Intensities of {Affymetrix GeneChip
                 3'} Expression Arrays Taking into Account Day-to-Day
                 Variability",
  journal =      j-TCBB,
  volume =       "8",
  number =       "5",
  pages =        "1425--1430",
  month =        sep,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.82",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Aug 17 09:10:05 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Aharoni:2011:QPD,
  author =       "Ehud Aharoni and Hani Neuvirth and Saharon Rosset",
  title =        "The Quality Preserving Database: {A} Computational
                 Framework for Encouraging Collaboration, Enhancing
                 Power and Controlling False Discovery",
  journal =      j-TCBB,
  volume =       "8",
  number =       "5",
  pages =        "1431--1437",
  month =        sep,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.105",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Wed Aug 17 09:10:05 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Axenopoulos:2011:SDF,
  author =       "Apostolos Axenopoulos and Petros Daras and Georgios
                 Papadopoulos and Elias Houstis",
  title =        "A Shape Descriptor for Fast Complementarity Matching
                 in Molecular Docking",
  journal =      j-TCBB,
  volume =       "8",
  number =       "6",
  pages =        "1441--1457",
  month =        nov,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.72",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Sun Nov 6 06:45:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Zhao:2011:ASM,
  author =       "Wenqi Zhao and Guoliang Xu and Chandrajit L. Bajaj",
  title =        "An Algebraic Spline Model of Molecular Surfaces for
                 Energetic Computations",
  journal =      j-TCBB,
  volume =       "8",
  number =       "6",
  pages =        "1458--1467",
  month =        nov,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.81",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Sun Nov 6 06:45:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Nebel:2011:AFE,
  author =       "Markus E. Nebel and Scheid Anika",
  title =        "Analysis of the Free Energy in a Stochastic {RNA}
                 Secondary Structure Model",
  journal =      j-TCBB,
  volume =       "8",
  number =       "6",
  pages =        "1468--1482",
  month =        nov,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.126",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Sun Nov 6 06:45:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Zhao:2011:ASB,
  author =       "Liang Zhao and Limsoon Wong and Jinyan Li",
  title =        "Antibody-Specified {B}-Cell Epitope Prediction in Line
                 with the Principle of Context-Awareness",
  journal =      j-TCBB,
  volume =       "8",
  number =       "6",
  pages =        "1483--1494",
  month =        nov,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.49",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Sun Nov 6 06:45:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Cobanoglu:2011:CGU,
  author =       "Murat Can Cobanoglu and Yucel Saygin and Ugur
                 Sezerman",
  title =        "Classification of {GPCRs} Using Family Specific
                 Motifs",
  journal =      j-TCBB,
  volume =       "8",
  number =       "6",
  pages =        "1495--1508",
  month =        nov,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.101",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Sun Nov 6 06:45:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Li:2011:CED,
  author =       "Qi Li and Chandra Kambhamettu",
  title =        "Contour Extraction of {{\em Drosophila}} Embryos",
  journal =      j-TCBB,
  volume =       "8",
  number =       "6",
  pages =        "1509--1521",
  month =        nov,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.37",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Sun Nov 6 06:45:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Oh:2011:FKD,
  author =       "Jung Hun Oh and Jean Gao",
  title =        "Fast Kernel Discriminant Analysis for Classification
                 of Liver Cancer Mass Spectra",
  journal =      j-TCBB,
  volume =       "8",
  number =       "6",
  pages =        "1522--1534",
  month =        nov,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.42",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Sun Nov 6 06:45:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Chen:2011:FAP,
  author =       "Qingfeng Chen and Yi-Ping Phoebe Chen",
  title =        "Function Annotation for Pseudoknot Using Structure
                 Similarity",
  journal =      j-TCBB,
  volume =       "8",
  number =       "6",
  pages =        "1535--1544",
  month =        nov,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.50",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Sun Nov 6 06:45:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Chalkidis:2011:HPH,
  author =       "Georgios Chalkidis and Masao Nagasaki and Satoru
                 Miyano",
  title =        "High Performance Hybrid Functional {Petri} Net
                 Simulations of Biological Pathway Models on {CUDA}",
  journal =      j-TCBB,
  volume =       "8",
  number =       "6",
  pages =        "1545--1556",
  month =        nov,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.118",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Sun Nov 6 06:45:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Li:2011:HHT,
  author =       "Helong Li and Sam Kwong and Lihua Yang and Daren Huang
                 and Dongping Xiao",
  title =        "{Hilbert--Huang Transform} for Analysis of Heart Rate
                 Variability in Cardiac Health",
  journal =      j-TCBB,
  volume =       "8",
  number =       "6",
  pages =        "1557--1567",
  month =        nov,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.43",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Sun Nov 6 06:45:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Sheng:2011:IAG,
  author =       "Jinhua Sheng and Hong-Wen Deng and Vince Calhoun and
                 Yu-Ping Wang",
  title =        "Integrated Analysis of Gene Expression and Copy Number
                 Data on Gene Shaving Using Independent Component
                 Analysis",
  journal =      j-TCBB,
  volume =       "8",
  number =       "6",
  pages =        "1568--1579",
  month =        nov,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.71",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Sun Nov 6 06:45:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Chen:2011:MIS,
  author =       "Carla C. M. Chen and Holger Schwender and Jonthan
                 Keith and Robin Nunkesser and Kerrie Mengersen and
                 Paula Macrossan",
  title =        "Methods for Identifying {SNP} Interactions: {A} Review
                 on Variations of Logic Regression, Random Forest and
                 {Bayesian} Logistic Regression",
  journal =      j-TCBB,
  volume =       "8",
  number =       "6",
  pages =        "1580--1591",
  month =        nov,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.46",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Sun Nov 6 06:45:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Zheng:2011:MPD,
  author =       "Chun-Hou Zheng and Lei Zhang and Vincent To-Yee Ng and
                 Chi Keung Shiu and D. -S. Huang",
  title =        "Molecular Pattern Discovery Based on Penalized Matrix
                 Decomposition",
  journal =      j-TCBB,
  volume =       "8",
  number =       "6",
  pages =        "1592--1603",
  month =        nov,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.79",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Sun Nov 6 06:45:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{LeFaucheur:2011:NCS,
  author =       "Xavier {Le Faucheur} and Eli Hershkovits and Rina
                 Tannenbaum and Allen Tannenbaum",
  title =        "Nonparametric Clustering for Studying {RNA}
                 Conformations",
  journal =      j-TCBB,
  volume =       "8",
  number =       "6",
  pages =        "1604--1619",
  month =        nov,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.128",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Sun Nov 6 06:45:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Dotu:2011:LPS,
  author =       "Ivan Dotu and Manuel Cebrian and Pascal {Van
                 Hentenryck} and Peter Clote",
  title =        "On Lattice Protein Structure Prediction Revisited",
  journal =      j-TCBB,
  volume =       "8",
  number =       "6",
  pages =        "1620--1632",
  month =        nov,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.41",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Sun Nov 6 06:45:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Li:2011:RUP,
  author =       "Hong-Dong Li and Yi-Zeng Liang and Qing-Song Xu and
                 Dong-Sheng Cao and Bin-Bin Tan and Bai-Chuan Deng and
                 Chen-Chen Lin",
  title =        "Recipe for Uncovering Predictive Genes Using Support
                 Vector Machines Based on Model Population Analysis",
  journal =      j-TCBB,
  volume =       "8",
  number =       "6",
  pages =        "1633--1641",
  month =        nov,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.36",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Sun Nov 6 06:45:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Hafemeister:2011:SOP,
  author =       "Christoph Hafemeister and Roland Krause and Alexander
                 Schliep",
  title =        "Selecting Oligonucleotide Probes for Whole-Genome
                 Tiling Arrays with a Cross-Hybridization Potential",
  journal =      j-TCBB,
  volume =       "8",
  number =       "6",
  pages =        "1642--1652",
  month =        nov,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.39",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Sun Nov 6 06:45:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Fober:2011:SAL,
  author =       "Thomas Fober and Gerghei Glinca and Gerhard Klebe and
                 Eyke Hullermeier",
  title =        "Superposition and Alignment of Labeled Point Clouds",
  journal =      j-TCBB,
  volume =       "8",
  number =       "6",
  pages =        "1653--1666",
  month =        nov,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.42",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Sun Nov 6 06:45:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Tuller:2011:CEI,
  author =       "Tamir Tuller and Elchanan Mossel",
  title =        "Co-evolution Is Incompatible with the {Markov}
                 Assumption in Phylogenetics",
  journal =      j-TCBB,
  volume =       "8",
  number =       "6",
  pages =        "1667--1670",
  month =        nov,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.124",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Sun Nov 6 06:45:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Sun:2011:CFS,
  author =       "Bing-Yu Sun and Zhi-Hua Zhu and Jiuyong Li and Bin
                 Linghu",
  title =        "Combined Feature Selection and Cancer Prognosis Using
                 Support Vector Machine Regression",
  journal =      j-TCBB,
  volume =       "8",
  number =       "6",
  pages =        "1671--1677",
  month =        nov,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.119",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Sun Nov 6 06:45:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Liu:2011:CBA,
  author =       "Weiguo Liu and Bertil Schmidt and Wolfgang
                 Muller-Wittig",
  title =        "{CUDA-BLASTP}: Accelerating {BLASTP} on {CUDA}-Enabled
                 Graphics Hardware",
  journal =      j-TCBB,
  volume =       "8",
  number =       "6",
  pages =        "1678--1684",
  month =        nov,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.33",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Sun Nov 6 06:45:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Zhang:2011:GTS,
  author =       "Louxin Zhang",
  title =        "From Gene Trees to Species Trees {II}: Species Tree
                 Inference by Minimizing Deep Coalescence Events",
  journal =      j-TCBB,
  volume =       "8",
  number =       "6",
  pages =        "1685--1691",
  month =        nov,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.83",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Sun Nov 6 06:45:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Fellows:2011:HIC,
  author =       "Michael R. Fellows and Tzvika Hartman and Danny
                 Hermelin and Gad M. Landau and Frances Rosamond and
                 Liat Rozenberg",
  title =        "Haplotype Inference Constrained by Plausible Haplotype
                 Data",
  journal =      j-TCBB,
  volume =       "8",
  number =       "6",
  pages =        "1692--1699",
  month =        nov,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.72",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Sun Nov 6 06:45:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Ambroise:2011:IRP,
  author =       "Jerome Ambroise and Joachim Giard and Jean-Luc Gala
                 and Benoit Macq",
  title =        "Identification of Relevant Properties for Epitopes
                 Detection Using a Regression Model",
  journal =      j-TCBB,
  volume =       "8",
  number =       "6",
  pages =        "1700--1707",
  month =        nov,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.77",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Sun Nov 6 06:45:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Wang:2011:ICA,
  author =       "Qingguo Wang and Yi Shang and Dong Xu",
  title =        "Improving a Consensus Approach for Protein Structure
                 Selection by Removing Redundancy",
  journal =      j-TCBB,
  volume =       "8",
  number =       "6",
  pages =        "1708--1715",
  month =        nov,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.75",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Sun Nov 6 06:45:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Poleksic:2011:OWU,
  author =       "Aleksandar Poleksic",
  title =        "Optimizing a Widely Used Protein Structure Alignment
                 Measure in Expected Polynomial Time",
  journal =      j-TCBB,
  volume =       "8",
  number =       "6",
  pages =        "1716--1720",
  month =        nov,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.122",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Sun Nov 6 06:45:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Rodrigues:2011:PCE,
  author =       "Thiago de Souza Rodrigues and Fernanda Caldas Cardoso
                 and Santuza Maria Ribeiro Teixeira and Sergio Costa
                 Oliveira and Antonio Padua Braga",
  title =        "Protein Classification with Extended-Sequence Coding
                 by Sliding Window",
  journal =      j-TCBB,
  volume =       "8",
  number =       "6",
  pages =        "1721--1726",
  month =        nov,
  year =         "2011",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.78",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Sun Nov 6 06:45:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{DeBlasio:2012:MEM,
  author =       "Daniel DeBlasio and Jocelyne Bruand and Shaojie
                 Zhang",
  title =        "A Memory Efficient Method for Structure-Based {RNA}
                 Multiple Alignment",
  journal =      j-TCBB,
  volume =       "9",
  number =       "1",
  pages =        "1--11",
  month =        jan,
  year =         "2012",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.86",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Dec 15 08:25:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Pirola:2012:EAH,
  author =       "Yuri Pirola and Paola Bonizzoni and Tao Jiang",
  title =        "An Efficient Algorithm for Haplotype Inference on
                 Pedigrees with Recombinations and Mutations",
  journal =      j-TCBB,
  volume =       "9",
  number =       "1",
  pages =        "12--25",
  month =        jan,
  year =         "2012",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.51",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Dec 15 08:25:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Doyon:2012:EME,
  author =       "Jean-Philippe Doyon and Sylvie Hamel and Cedric
                 Chauve",
  title =        "An Efficient Method for Exploring the Space of Gene
                 Tree\slash Species Tree Reconciliations in a
                 Probabilistic Framework",
  journal =      j-TCBB,
  volume =       "9",
  number =       "1",
  pages =        "26--39",
  month =        jan,
  year =         "2012",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.64",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Dec 15 08:25:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Wang:2012:EMM,
  author =       "Chong Wang and Peter Beyerlein and Heike Pospisil and
                 Antje Krause and Chris Nugent and Werner Dubitzky",
  title =        "An Efficient Method for Modeling Kinetic Behavior of
                 Channel Proteins in Cardiomyocytes",
  journal =      j-TCBB,
  volume =       "9",
  number =       "1",
  pages =        "40--51",
  month =        jan,
  year =         "2012",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.84",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Dec 15 08:25:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Vasic:2012:ITA,
  author =       "Bane Vasic and Vida Ravanmehr and Anantha Raman
                 Krishnan",
  title =        "An Information Theoretic Approach to Constructing
                 Robust {Boolean} Gene Regulatory Networks",
  journal =      j-TCBB,
  volume =       "9",
  number =       "1",
  pages =        "52--65",
  month =        jan,
  year =         "2012",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.61",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Dec 15 08:25:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Piraveenan:2012:AMD,
  author =       "Mahendra Piraveenan and Mikhail Prokopenko and Albert
                 Zomaya",
  title =        "Assortative Mixing in Directed Biological Networks",
  journal =      j-TCBB,
  volume =       "9",
  number =       "1",
  pages =        "66--78",
  month =        jan,
  year =         "2012",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.80",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Dec 15 08:25:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Chan:2012:CVM,
  author =       "Raymond H. Chan and Tony H. Chan and Hau Man Yeung and
                 Roger Wei Wang",
  title =        "Composition Vector Method Based on Maximum Entropy
                 Principle for Sequence Comparison",
  journal =      j-TCBB,
  volume =       "9",
  number =       "1",
  pages =        "79--87",
  month =        jan,
  year =         "2012",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.45",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Dec 15 08:25:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Quevedo:2012:DLP,
  author =       "Jose R. Quevedo and Antonio Bahamonde and Miguel
                 Perez-Enciso and Oscar Luaces",
  title =        "Disease Liability Prediction from Large Scale
                 Genotyping Data Using Classifiers with a Reject
                 Option",
  journal =      j-TCBB,
  volume =       "9",
  number =       "1",
  pages =        "88--97",
  month =        jan,
  year =         "2012",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.44",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Dec 15 08:25:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Li:2012:DGE,
  author =       "Ying-Xin Li and Shuiwang Ji and Sudhir Kumar and
                 Jieping Ye and Zhi-Hua Zhou",
  title =        "{{\em Drosophila}} Gene Expression Pattern Annotation
                 through Multi-Instance Multi-Label Learning",
  journal =      j-TCBB,
  volume =       "9",
  number =       "1",
  pages =        "98--112",
  month =        jan,
  year =         "2012",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.73",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Dec 15 08:25:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Paoletti:2012:INC,
  author =       "David R. Paoletti and Dan E. Krane and Michael L.
                 Raymer and Travis E. Doom",
  title =        "Inferring the Number of Contributors to Mixed {DNA}
                 Profiles",
  journal =      j-TCBB,
  volume =       "9",
  number =       "1",
  pages =        "113--122",
  month =        jan,
  year =         "2012",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.76",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Dec 15 08:25:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Qian:2012:IGR,
  author =       "Xiaoning Qian and Edward R. Dougherty",
  title =        "Intervention in Gene Regulatory Networks via
                 Phenotypically Constrained Control Policies Based on
                 Long-Run Behavior",
  journal =      j-TCBB,
  volume =       "9",
  number =       "1",
  pages =        "123--136",
  month =        jan,
  year =         "2012",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.107",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Dec 15 08:25:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Kuruppu:2012:IDC,
  author =       "Shanika Kuruppu and Bryan Beresford-Smith and Thomas
                 Conway and Justin Zobel",
  title =        "Iterative Dictionary Construction for Compression of
                 Large {DNA} Data Sets",
  journal =      j-TCBB,
  volume =       "9",
  number =       "1",
  pages =        "137--149",
  month =        jan,
  year =         "2012",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.82",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Dec 15 08:25:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Bogdanowicz:2012:MSD,
  author =       "Damian Bogdanowicz and Krzysztof Giaro",
  title =        "Matching Split Distance for Unrooted Binary
                 Phylogenetic Trees",
  journal =      j-TCBB,
  volume =       "9",
  number =       "1",
  pages =        "150--160",
  month =        jan,
  year =         "2012",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.48",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Dec 15 08:25:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Wong:2012:MEA,
  author =       "Thomas K. F. Wong and Y. S. Chiu and T. W. Lam and S.
                 M. Yiu",
  title =        "Memory Efficient Algorithms for Structural Alignment
                 of {RNAs} with Pseudoknots",
  journal =      j-TCBB,
  volume =       "9",
  number =       "1",
  pages =        "161--168",
  month =        jan,
  year =         "2012",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.66",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Dec 15 08:25:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Mahoney:2012:MOB,
  author =       "Arthur W. Mahoney and Gregory J. Podgorski and
                 Nicholas S. Flann",
  title =        "Multiobjective Optimization Based-Approach for
                 Discovering Novel Cancer Therapies",
  journal =      j-TCBB,
  volume =       "9",
  number =       "1",
  pages =        "169--184",
  month =        jan,
  year =         "2012",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2010.39",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Dec 15 08:25:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Sun:2012:PEU,
  author =       "Jianyong Sun and Jonathan M. Garibaldi and Charlie
                 Hodgman",
  title =        "Parameter Estimation Using Metaheuristics in Systems
                 Biology: {A} Comprehensive Review",
  journal =      j-TCBB,
  volume =       "9",
  number =       "1",
  pages =        "185--202",
  month =        jan,
  year =         "2012",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.63",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Dec 15 08:25:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Passerini:2012:PMB,
  author =       "Andrea Passerini and Marco Lippi and Paolo Frasconi",
  title =        "Predicting Metal-Binding Sites from Protein Sequence",
  journal =      j-TCBB,
  volume =       "9",
  number =       "1",
  pages =        "203--213",
  month =        jan,
  year =         "2012",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.94",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Dec 15 08:25:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Bokhari:2012:RNE,
  author =       "Shahid H. Bokhari and Laura W. Pomeroy and Daniel A.
                 Janies",
  title =        "Reassortment Networks and the Evolution of Pandemic
                 {H1N1} Swine-Origin Influenza",
  journal =      j-TCBB,
  volume =       "9",
  number =       "1",
  pages =        "214--227",
  month =        jan,
  year =         "2012",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.95",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Dec 15 08:25:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Ma:2012:RNH,
  author =       "Lingyu Ma and Marco Reisert and Hans Burkhardt",
  title =        "{RENNSH}: {A} Novel $ \alpha $-Helix Identification
                 Approach for Intermediate Resolution Electron Density
                 Maps",
  journal =      j-TCBB,
  volume =       "9",
  number =       "1",
  pages =        "228--239",
  month =        jan,
  year =         "2012",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.52",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Dec 15 08:25:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Li:2012:RSH,
  author =       "Shuai Cheng Li and Dongbo Bu and Ming Li",
  title =        "Residues with Similar Hexagon Neighborhoods Share
                 Similar Side-Chain Conformations",
  journal =      j-TCBB,
  volume =       "9",
  number =       "1",
  pages =        "240--248",
  month =        jan,
  year =         "2012",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.74",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Dec 15 08:25:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Sun:2012:SSM,
  author =       "Hong Sun and Ahmet Sacan and Hakan Ferhatosmanoglu and
                 Yusu Wang",
  title =        "{Smolign}: {A} Spatial Motifs-Based Protein Multiple
                 Structural Alignment Method",
  journal =      j-TCBB,
  volume =       "9",
  number =       "1",
  pages =        "249--261",
  month =        jan,
  year =         "2012",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.67",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Dec 15 08:25:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Yu:2012:SGS,
  author =       "Lei Yu and Yue Han and Michael E. Berens",
  title =        "Stable Gene Selection from Microarray Data via Sample
                 Weighting",
  journal =      j-TCBB,
  volume =       "9",
  number =       "1",
  pages =        "262--272",
  month =        jan,
  year =         "2012",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.47",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Dec 15 08:25:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Bonet:2012:CFM,
  author =       "Maria Luisa Bonet and Simone Linz and Katherine St.
                 John",
  title =        "The Complexity of Finding Multiple Solutions to
                 Betweenness and Quartet Compatibility",
  journal =      j-TCBB,
  volume =       "9",
  number =       "1",
  pages =        "273--285",
  month =        jan,
  year =         "2012",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.108",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Dec 15 08:25:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Daniels:2012:TPS,
  author =       "Noah Daniels and Anoop Kumar and Lenore Cowen and Matt
                 Menke",
  title =        "Touring Protein Space with {Matt}",
  journal =      j-TCBB,
  volume =       "9",
  number =       "1",
  pages =        "286--293",
  month =        jan,
  year =         "2012",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.70",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Dec 15 08:25:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Xiang:2012:TDT,
  author =       "Yang Xiang and Philip R. O. Payne and Kun Huang",
  title =        "Transactional Database Transformation and Its
                 Application in Prioritizing Human Disease Genes",
  journal =      j-TCBB,
  volume =       "9",
  number =       "1",
  pages =        "294--304",
  month =        jan,
  year =         "2012",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.58",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Dec 15 08:25:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Ambert:2012:IGS,
  author =       "Kyle H. Ambert and Aaron M. Cohen",
  title =        "$k$-Information Gain Scaled Nearest Neighbors: {A}
                 Novel Approach to Classifying Protein-Protein
                 Interaction-Related Documents",
  journal =      j-TCBB,
  volume =       "9",
  number =       "1",
  pages =        "305--310",
  month =        jan,
  year =         "2012",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.32",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Dec 15 08:25:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Xu:2012:MLM,
  author =       "Yun Xu and Da Teng and Yiming Lei",
  title =        "{MinePhos}: {A} Literature Mining System for Protein
                 Phoshphorylation Information Extraction",
  journal =      j-TCBB,
  volume =       "9",
  number =       "1",
  pages =        "311--315",
  month =        jan,
  year =         "2012",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.85",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Dec 15 08:25:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Xu:2012:RL,
  author =       "Yun Xu and Da Teng and Yiming Lei",
  title =        "2011 Reviewers List",
  journal =      j-TCBB,
  volume =       "9",
  number =       "1",
  pages =        "316--318",
  month =        jan,
  year =         "2012",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2012.2",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Dec 15 08:25:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Xu:2012:AI,
  author =       "Yun Xu and Da Teng and Yiming Lei",
  title =        "2011 Annual Index",
  journal =      j-TCBB,
  volume =       "9",
  number =       "1",
  pages =        "??--??",
  month =        jan,
  year =         "2012",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2012.1",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Dec 15 08:25:50 MST 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Acharya:2012:GCA,
  author =       "Lipi Acharya and Thair Judeh and Zhansheng Duan and
                 Michael Rabbat and Dongxiao Zhu",
  title =        "{GSGS}: {A} Computational Approach to Reconstruct
                 Signaling Pathway Structures from Gene Sets",
  journal =      j-TCBB,
  volume =       "9",
  number =       "2",
  pages =        "438--450",
  month =        mar,
  year =         "2012",
  CODEN =        "ITCBCY",
  DOI =          "http://dx.doi.org/10.1109/TCBB.2011.143",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Jan 26 16:30:44 2012",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Reconstruction of signaling pathway structures is
                 essential to decipher complex regulatory relationships
                 in living cells. The existing computational approaches
                 often rely on unrealistic biological assumptions and do
                 not explicitly consider signal transduction mechanisms.
                 Signal transduction events refer to linear cascades of
                 reactions from the cell surface to the nucleus and
                 characterize a signaling pathway. In this paper, we
                 propose a novel approach, Gene Set Gibbs Sampling
                 (GSGS), to reverse engineer signaling pathway
                 structures from gene sets related to the pathways. We
                 hypothesize that signaling pathways are structurally an
                 ensemble of overlapping linear signal transduction
                 events which we encode as Information Flows (IFs).",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}

@Article{Andreotti:2012:ALR,
  author =       "Sandro Andreotti and Gunnar W. Klau and Knut Reinert",
  title =        "{Antilope} --- a {Lagrangian} Relaxation Approach to
                 th