%%% -*-BibTeX-*- %%% ==================================================================== %%% BibTeX-file{ %%% author = "Nelson H. F. Beebe", %%% version = "1.12", %%% date = "04 July 2008", %%% time = "09:05:17 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 = "53443 5842 29346 264976", %%% 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), covering all journal issues %%% from 2004 to date. %%% %%% At version 1.12, the COMPLETE journal %%% coverage looked like this: %%% %%% 2004 ( 23) 2006 ( 41) 2008 ( 30) %%% 2005 ( 37) 2007 ( 69) %%% %%% Article: 200 %%% %%% Total entries: 200 %%% %%% 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." %%% } %%% ==================================================================== @Preamble{"\input bibnames.sty"} %%% ==================================================================== %%% 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/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", bibdate = "Mon Nov 22 06:42:56 MST 2004", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Mon Nov 22 06:42:56 MST 2004", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Mon Nov 22 06:42:56 MST 2004", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Mon Nov 22 06:42:56 MST 2004", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Mon Nov 22 06:42:56 MST 2004", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Mon Nov 22 06:42:56 MST 2004", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Mon Nov 22 06:42:56 MST 2004", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Mon Nov 22 06:42:56 MST 2004", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Mon Nov 22 06:42:56 MST 2004", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Mon Nov 22 06:42:56 MST 2004", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Mon Nov 22 06:42:56 MST 2004", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Mon Jan 24 14:15:55 MST 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Mon Jan 24 14:15:55 MST 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Mon Jan 24 14:15:55 MST 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Mon Jan 24 14:15:55 MST 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Mon Jan 24 14:15:55 MST 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Mon Jan 24 14:15:55 MST 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Mon Jan 24 14:15:55 MST 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Mon Jan 24 14:15:55 MST 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Mon Jan 24 14:15:55 MST 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Mon Jan 24 14:15:55 MST 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Mon Jan 24 14:15:55 MST 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Mon Jan 24 14:15:55 MST 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Tue Apr 12 07:11:54 MDT 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Tue Apr 12 07:11:54 MDT 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Tue Apr 12 07:11:54 MDT 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Tue Apr 12 07:11:54 MDT 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Tue Apr 12 07:11:54 MDT 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Tue Apr 12 07:11:54 MDT 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Tue Apr 12 07:11:54 MDT 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Tue Apr 12 07:11:54 MDT 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @Article{Staff:2005:RL, author = "{IEEE/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", bibdate = "Tue Apr 12 07:11:54 MDT 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Wed Jun 22 17:33:35 MDT 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Wed Jun 22 17:33:35 MDT 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Wed Jun 22 17:33:35 MDT 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Wed Jun 22 17:33:35 MDT 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Wed Jun 22 17:33:35 MDT 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Wed Jun 22 17:33:35 MDT 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Wed Jun 22 17:33:35 MDT 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Wed Jun 22 17:33:35 MDT 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Wed Jun 22 17:33:35 MDT 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Tue Sep 20 06:11:25 MDT 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Tue Sep 20 06:11:25 MDT 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Tue Sep 20 06:11:25 MDT 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Tue Sep 20 06:11:25 MDT 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Tue Sep 20 06:11:25 MDT 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Tue Sep 20 06:11:25 MDT 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Tue Sep 20 06:11:25 MDT 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Tue Sep 20 06:11:25 MDT 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Tue Sep 20 06:11:25 MDT 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Tue Sep 20 06:11:25 MDT 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Fri Nov 18 05:22:15 MST 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Fri Nov 18 05:22:15 MST 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Fri Nov 18 05:22:15 MST 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Fri Nov 18 05:22:15 MST 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Fri Nov 18 05:22:15 MST 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Fri Nov 18 05:22:15 MST 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Fri Nov 18 05:22:15 MST 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Fri Nov 18 05:22:15 MST 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Fri Nov 18 05:22:15 MST 2005", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Thu Feb 16 11:06:15 MST 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Thu Feb 16 11:06:15 MST 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Thu Feb 16 11:06:15 MST 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Thu Feb 16 11:06:15 MST 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", ISSN = "1545-5963", bibdate = "Thu Feb 16 11:06:15 MST 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Thu Feb 16 11:06:15 MST 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Thu Feb 16 11:06:15 MST 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Thu Feb 16 11:06:15 MST 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Thu Feb 16 11:06:15 MST 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Thu Feb 16 11:06:15 MST 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Wed Jun 7 06:38:18 MDT 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Wed Jun 7 06:38:18 MDT 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Wed Jun 7 06:38:18 MDT 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Wed Jun 7 06:38:18 MDT 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Wed Jun 7 06:38:18 MDT 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Wed Jun 7 06:38:18 MDT 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Wed Jun 7 06:38:18 MDT 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Wed Jun 7 06:38:18 MDT 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Wed Jun 7 06:38:18 MDT 2006", bibsource = "http://portal.acm.org/", 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, } @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", bibdate = "Mon Sep 11 07:36:29 MDT 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Mon Sep 11 07:36:29 MDT 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Mon Sep 11 07:36:29 MDT 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Mon Sep 11 07:36:29 MDT 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Mon Sep 11 07:36:29 MDT 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Mon Sep 11 07:36:29 MDT 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Mon Sep 11 07:36:29 MDT 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Mon Sep 11 07:36:29 MDT 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Mon Sep 11 07:36:29 MDT 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Mon Sep 11 07:36:29 MDT 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Mon Sep 11 07:36:29 MDT 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Thu Nov 30 19:05:58 MST 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Thu Nov 30 19:05:58 MST 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Thu Nov 30 19:05:58 MST 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Thu Nov 30 19:05:58 MST 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Thu Nov 30 19:05:58 MST 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Thu Nov 30 19:05:58 MST 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Thu Nov 30 19:05:58 MST 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Thu Nov 30 19:05:58 MST 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Thu Nov 30 19:05:58 MST 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Thu Nov 30 19:05:58 MST 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Thu Nov 30 19:05:58 MST 2006", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Thu Jun 12 16:57:20 MDT 2008", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Thu Jun 12 16:57:20 MDT 2008", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Thu Jun 12 16:57:20 MDT 2008", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Thu Jun 12 16:57:20 MDT 2008", bibsource = "http://portal.acm.org/", 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, 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", bibdate = "Thu Jun 12 16:57:20 MDT 2008", bibsource = "http://portal.acm.org/", 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| log|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, 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", bibdate = "Thu Jun 12 16:57:20 MDT 2008", bibsource = "http://portal.acm.org/", 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, 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", bibdate = "Thu Jun 12 16:57:20 MDT 2008", bibsource = "http://portal.acm.org/", 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, 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", bibdate = "Thu Jun 12 16:57:20 MDT 2008", bibsource = "http://portal.acm.org/", 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, 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", bibdate = "Thu Jun 12 16:57:20 MDT 2008", bibsource = "http://portal.acm.org/", 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/PSO for multiclass cancer diagnosis is demonstrated by testing it on three publicly available multiple-class cancer data sets. ssEAM/PSO achieves competitive performance on all these data sets, with results comparable to or better than those obtained by other classifiers.", acknowledgement = ack-nhfb, 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", bibdate = "Thu Jun 12 16:57:20 MDT 2008", bibsource = "http://portal.acm.org/", 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 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 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 http://ppi.cse.nd.edu.", acknowledgement = ack-nhfb, 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", bibdate = "Thu Jun 12 16:57:20 MDT 2008", bibsource = "http://portal.acm.org/", 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, 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", bibdate = "Thu Jun 12 16:57:20 MDT 2008", bibsource = "http://portal.acm.org/", 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, 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", bibdate = "Thu Jun 12 16:57:20 MDT 2008", bibsource = "http://portal.acm.org/", 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, 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", bibdate = "Thu Jun 12 16:57:20 MDT 2008", bibsource = "http://portal.acm.org/", 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, 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", bibdate = "Thu Jun 12 16:57:20 MDT 2008", bibsource = "http://portal.acm.org/", 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, 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", bibdate = "Thu Jun 12 16:57:20 MDT 2008", bibsource = "http://portal.acm.org/", 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, 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", bibdate = "Thu Jun 12 16:57:20 MDT 2008", bibsource = "http://portal.acm.org/", 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, 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", bibdate = "Thu Jun 12 16:57:20 MDT 2008", bibsource = "http://portal.acm.org/", 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, } @Article{Biology:2007:RL, author = "IEEE/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", bibdate = "Thu Jun 12 16:57:20 MDT 2008", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Thu Jun 12 16:57:55 MDT 2008", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Thu Jun 12 16:57:55 MDT 2008", bibsource = "http://portal.acm.org/", 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, 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", bibdate = "Thu Jun 12 16:57:55 MDT 2008", bibsource = "http://portal.acm.org/", 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, 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", bibdate = "Thu Jun 12 16:57:55 MDT 2008", bibsource = "http://portal.acm.org/", 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, 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", bibdate = "Thu Jun 12 16:57:55 MDT 2008", bibsource = "http://portal.acm.org/", 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, 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", bibdate = "Thu Jun 12 16:57:55 MDT 2008", bibsource = "http://portal.acm.org/", 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, 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", bibdate = "Thu Jun 12 16:57:55 MDT 2008", bibsource = "http://portal.acm.org/", 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, 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", bibdate = "Thu Jun 12 16:57:55 MDT 2008", bibsource = "http://portal.acm.org/", 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, 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", bibdate = "Thu Jun 12 16:57:55 MDT 2008", bibsource = "http://portal.acm.org/", 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, 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", bibdate = "Thu Jun 12 16:57:55 MDT 2008", bibsource = "http://portal.acm.org/", 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, 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", bibdate = "Thu Jun 12 16:57:55 MDT 2008", bibsource = "http://portal.acm.org/", 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, 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", bibdate = "Thu Jun 12 16:57:55 MDT 2008", bibsource = "http://portal.acm.org/", 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, 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", bibdate = "Thu Jun 12 16:57:55 MDT 2008", bibsource = "http://portal.acm.org/", 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, 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", bibdate = "Thu Jun 12 16:57:55 MDT 2008", bibsource = "http://portal.acm.org/", 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, 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", bibdate = "Thu Jun 12 16:58:24 MDT 2008", bibsource = "http://portal.acm.org/", acknowledgement = ack-nhfb, } @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", bibdate = "Thu Jun 12 16:58:24 MDT 2008", bibsource = "http://portal.acm.org/", 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,