QUBIC: a qualitative biclustering algorithm for analyses of gene expression data
Biclustering extends the traditional clustering techniques by attempting to find (all) subgroups of genes with similar expression patterns under to-be-identified subsets of experimental conditions when applied to gene expression data. Still the real power of this clustering strategy is yet to be ful...
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| Published in | Nucleic acids research Vol. 37; no. 15; p. e101 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Oxford University Press
01.08.2009
Oxford Publishing Limited (England) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0305-1048 1362-4962 1362-4954 1362-4962 |
| DOI | 10.1093/nar/gkp491 |
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| Abstract | Biclustering extends the traditional clustering techniques by attempting to find (all) subgroups of genes with similar expression patterns under to-be-identified subsets of experimental conditions when applied to gene expression data. Still the real power of this clustering strategy is yet to be fully realized due to the lack of effective and efficient algorithms for reliably solving the general biclustering problem. We report a QUalitative BIClustering algorithm (QUBIC) that can solve the biclustering problem in a more general form, compared to existing algorithms, through employing a combination of qualitative (or semi-quantitative) measures of gene expression data and a combinatorial optimization technique. One key unique feature of the QUBIC algorithm is that it can identify all statistically significant biclusters including biclusters with the so-called 'scaling patterns', a problem considered to be rather challenging; another key unique feature is that the algorithm solves such general biclustering problems very efficiently, capable of solving biclustering problems with tens of thousands of genes under up to thousands of conditions in a few minutes of the CPU time on a desktop computer. We have demonstrated a considerably improved biclustering performance by our algorithm compared to the existing algorithms on various benchmark sets and data sets of our own. QUBIC was written in ANSI C and tested using GCC (version 4.1.2) on Linux. Its source code is available at: http://csbl.bmb.uga.edu/~maqin/bicluster. A server version of QUBIC is also available upon request. |
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| AbstractList | Biclustering extends the traditional clustering techniques by attempting to find (all) subgroups of genes with similar expression patterns under to-be-identified subsets of experimental conditions when applied to gene expression data. Still the real power of this clustering strategy is yet to be fully realized due to the lack of effective and efficient algorithms for reliably solving the general biclustering problem. We report a QUalitative BIClustering algorithm (QUBIC) that can solve the biclustering problem in a more general form, compared to existing algorithms, through employing a combination of qualitative (or semi-quantitative) measures of gene expression data and a combinatorial optimization technique. One key unique feature of the QUBIC algorithm is that it can identify all statistically significant biclusters including biclusters with the so-called 'scaling patterns', a problem considered to be rather challenging; another key unique feature is that the algorithm solves such general biclustering problems very efficiently, capable of solving biclustering problems with tens of thousands of genes under up to thousands of conditions in a few minutes of the CPU time on a desktop computer. We have demonstrated a considerably improved biclustering performance by our algorithm compared to the existing algorithms on various benchmark sets and data sets of our own. QUBIC was written in ANSI C and tested using GCC (version 4.1.2) on Linux. Its source code is available at: http://csbl.bmb.uga.edu/~maqin/bicluster. A server version of QUBIC is also available upon request. Biclustering extends the traditional clustering techniques by attempting to find (all) subgroups of genes with similar expression patterns under to-be-identified subsets of experimental conditions when applied to gene expression data. Still the real power of this clustering strategy is yet to be fully realized due to the lack of effective and efficient algorithms for reliably solving the general biclustering problem. We report a QUalitative BIClustering algorithm (QUBIC) that can solve the biclustering problem in a more general form, compared to existing algorithms, through employing a combination of qualitative (or semi-quantitative) measures of gene expression data and a combinatorial optimization technique. One key unique feature of the QUBIC algorithm is that it can identify all statistically significant biclusters including biclusters with the so-called 'scaling patterns', a problem considered to be rather challenging; another key unique feature is that the algorithm solves such general biclustering problems very efficiently, capable of solving biclustering problems with tens of thousands of genes under up to thousands of conditions in a few minutes of the CPU time on a desktop computer. We have demonstrated a considerably improved biclustering performance by our algorithm compared to the existing algorithms on various benchmark sets and data sets of our own. QUBIC was written in ANSI C and tested using GCC (version 4.1.2) on Linux. Its source code is available at: http://csbl.bmb.uga.edu/ similar to maqin/bicluster. A server version of QUBIC is also available upon request. Biclustering extends the traditional clustering techniques by attempting to find (all) subgroups of genes with similar expression patterns under to-be-identified subsets of experimental conditions when applied to gene expression data. Still the real power of this clustering strategy is yet to be fully realized due to the lack of effective and efficient algorithms for reliably solving the general biclustering problem. We report a QUalitative BIClustering algorithm (QUBIC) that can solve the biclustering problem in a more general form, compared to existing algorithms, through employing a combination of qualitative (or semi-quantitative) measures of gene expression data and a combinatorial optimization technique. One key unique feature of the QUBIC algorithm is that it can identify all statistically significant biclusters including biclusters with the so-called ‘scaling patterns’, a problem considered to be rather challenging; another key unique feature is that the algorithm solves such general biclustering problems very efficiently, capable of solving biclustering problems with tens of thousands of genes under up to thousands of conditions in a few minutes of the CPU time on a desktop computer. We have demonstrated a considerably improved biclustering performance by our algorithm compared to the existing algorithms on various benchmark sets and data sets of our own. QUBIC was written in ANSI C and tested using GCC (version 4.1.2) on Linux. Its source code is available at: http://csbl.bmb.uga.edu/∼maqin/bicluster. A server version of QUBIC is also available upon request. Biclustering extends the traditional clustering techniques by attempting to find (all) subgroups of genes with similar expression patterns under to-be-identified subsets of experimental conditions when applied to gene expression data. Still the real power of this clustering strategy is yet to be fully realized due to the lack of effective and efficient algorithms for reliably solving the general biclustering problem. We report a QUalitative BIClustering algorithm (QUBIC) that can solve the biclustering problem in a more general form, compared to existing algorithms, through employing a combination of qualitative (or semi-quantitative) measures of gene expression data and a combinatorial optimization technique. One key unique feature of the QUBIC algorithm is that it can identify all statistically significant biclusters including biclusters with the so-called 'scaling patterns', a problem considered to be rather challenging; another key unique feature is that the algorithm solves such general biclustering problems very efficiently, capable of solving biclustering problems with tens of thousands of genes under up to thousands of conditions in a few minutes of the CPU time on a desktop computer. We have demonstrated a considerably improved biclustering performance by our algorithm compared to the existing algorithms on various benchmark sets and data sets of our own. QUBIC was written in ANSI C and tested using GCC (version 4.1.2) on Linux. Its source code is available at: http://csbl.bmb.uga.edu/ approximately maqin/bicluster. A server version of QUBIC is also available upon request.Biclustering extends the traditional clustering techniques by attempting to find (all) subgroups of genes with similar expression patterns under to-be-identified subsets of experimental conditions when applied to gene expression data. Still the real power of this clustering strategy is yet to be fully realized due to the lack of effective and efficient algorithms for reliably solving the general biclustering problem. We report a QUalitative BIClustering algorithm (QUBIC) that can solve the biclustering problem in a more general form, compared to existing algorithms, through employing a combination of qualitative (or semi-quantitative) measures of gene expression data and a combinatorial optimization technique. One key unique feature of the QUBIC algorithm is that it can identify all statistically significant biclusters including biclusters with the so-called 'scaling patterns', a problem considered to be rather challenging; another key unique feature is that the algorithm solves such general biclustering problems very efficiently, capable of solving biclustering problems with tens of thousands of genes under up to thousands of conditions in a few minutes of the CPU time on a desktop computer. We have demonstrated a considerably improved biclustering performance by our algorithm compared to the existing algorithms on various benchmark sets and data sets of our own. QUBIC was written in ANSI C and tested using GCC (version 4.1.2) on Linux. Its source code is available at: http://csbl.bmb.uga.edu/ approximately maqin/bicluster. A server version of QUBIC is also available upon request. Biclustering extends the traditional clustering techniques by attempting to find (all) subgroups of genes with similar expression patterns under to-be-identified subsets of experimental conditions when applied to gene expression data. Still the real power of this clustering strategy is yet to be fully realized due to the lack of effective and efficient algorithms for reliably solving the general biclustering problem. We report a QUalitative BIClustering algorithm (QUBIC) that can solve the biclustering problem in a more general form, compared to existing algorithms, through employing a combination of qualitative (or semi-quantitative) measures of gene expression data and a combinatorial optimization technique. One key unique feature of the QUBIC algorithm is that it can identify all statistically significant biclusters including biclusters with the so-called 'scaling patterns', a problem considered to be rather challenging; another key unique feature is that the algorithm solves such general biclustering problems very efficiently, capable of solving biclustering problems with tens of thousands of genes under up to thousands of conditions in a few minutes of the CPU time on a desktop computer. We have demonstrated a considerably improved biclustering performance by our algorithm compared to the existing algorithms on various benchmark sets and data sets of our own. QUBIC was written in ANSI C and tested using GCC (version 4.1.2) on Linux. Its source code is available at: http://csbl.bmb.uga.edu/ approximately maqin/bicluster. A server version of QUBIC is also available upon request. |
| Author | Xu, Ying Paterson, Andrew H. Tang, Haibao Li, Guojun Ma, Qin |
| AuthorAffiliation | 1 Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, and Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA, 2 School of Mathematics, Shandong University, Jinan 250100, China, 3 Department of Plant Biology, University of Georgia, USA and 4 College of Computer Science and Technology, Jilin University, Changchun, China |
| AuthorAffiliation_xml | – name: 1 Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, and Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA, 2 School of Mathematics, Shandong University, Jinan 250100, China, 3 Department of Plant Biology, University of Georgia, USA and 4 College of Computer Science and Technology, Jilin University, Changchun, China |
| Author_xml | – sequence: 1 fullname: Li, Guojun – sequence: 2 fullname: Ma, Qin – sequence: 3 fullname: Tang, Haibao – sequence: 4 fullname: Paterson, Andrew H – sequence: 5 fullname: Xu, Ying |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/19509312$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1091/mbc.11.12.4241 10.1142/S0219720006002065 10.1093/bioinformatics/btl560 10.1186/1471-2105-8-250 10.1093/nar/gki108 10.1186/1471-2105-7-280 10.1089/10665270360688075 10.1093/bioinformatics/18.suppl_1.S136 10.1002/0470857897.ch8 10.1073/pnas.95.25.14863 10.1109/TCBB.2004.2 10.1093/bioinformatics/bti641 10.1038/75556 10.1093/bioinformatics/btl099 10.1186/1471-2105-6-232 10.1093/bioinformatics/btl060 10.1073/pnas.210134797 10.1093/bioinformatics/bth166 10.1093/nar/gkh894 10.1093/bioinformatics/18.3.413 10.1080/01621459.1963.10500855 10.1109/TITB.2006.872073 10.1038/ng765 10.1101/gr.648603 10.1126/science.286.5439.531 10.1186/gb-2003-4-5-r34 10.1080/01621459.1972.10481214 10.1186/1471-2164-9-S2-S20 10.1142/S021972000600217X 10.1186/1471-2105-7-78 10.1093/nar/gkm815 10.1093/bioinformatics/btg114 |
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| References | Kluger ( key 20170510120623_B34) 2003; 13 Eisen ( key 20170510120623_B3) 1998; 95 Kung ( key 20170510120623_B16) 2006; 4 Bryan ( key 20170510120623_B9) 2008; 9 Castillo-Davis ( key 20170510120623_B31) 2003; 19 Golub ( key 20170510120623_B36) 1999; 286 Barkow ( key 20170510120623_B25) 2006; 22 Ben-dor ( key 20170510120623_B12) 2003; 10 Faith ( key 20170510120623_B27) 2008; 36 Keseler ( key 20170510120623_B30) 2005; 33 Ruepp ( key 20170510120623_B33) 2004; 32 Bryan ( key 20170510120623_B10) 2006; 10 Huttenhower ( key 20170510120623_B21) 2007; 8 Ashburner ( key 20170510120623_B28) 2000; 25 Xu ( key 20170510120623_B4) 2002; 18 Bryan ( key 20170510120623_B19) 2006; 10 Getz ( key 20170510120623_B7) 2000; 97 Kanehisa ( key 20170510120623_B29) 2002; 247 Madeira ( key 20170510120623_B23) 2004; 1 Shamir ( key 20170510120623_B26) 2005; 6 Ihmels ( key 20170510120623_B20) 2004; 20 Tanay ( key 20170510120623_B14) 2002; 18 Yeung ( key 20170510120623_B1) 2003; 4 Prelic ( key 20170510120623_B13) 2006; 22 Gasch ( key 20170510120623_B32) 2000; 11 Liu ( key 20170510120623_B22) 2007; 23 Murali ( key 20170510120623_B24) 2003; 8 Cheng ( key 20170510120623_B8) 2000; 8 McLachlan ( key 20170510120623_B2) 2002; 18 Hartigan ( key 20170510120623_B6) 1972; 67 Armstrong ( key 20170510120623_B35) 2002; 30 Reiss ( key 20170510120623_B11) 2006; 7 Aguilar-Ruiz ( key 20170510120623_B15) 2005; 21 Carmona-Saez ( key 20170510120623_B18) 2006; 7 Morgan ( key 20170510120623_B5) 1963; 58 Li ( key 20170510120623_B17) 2006; 4 16176576 - BMC Bioinformatics. 2005;6:232 16819784 - J Bioinform Comput Biol. 2006 Apr;4(2):275-98 12016051 - Bioinformatics. 2002 Apr;18(4):536-45 17090578 - Bioinformatics. 2007 Jan 1;23(1):50-6 11934740 - Bioinformatics. 2002 Mar;18(3):413-22 15044247 - Bioinformatics. 2004 Sep 1;20(13):1993-2003 16871720 - IEEE Trans Inf Technol Biomed. 2006 Jul;10(3):519-25 15486203 - Nucleic Acids Res. 2004;32(18):5539-45 10977070 - Proc Int Conf Intell Syst Mol Biol. 2000;8:93-103 9843981 - Proc Natl Acad Sci U S A. 1998 Dec 8;95(25):14863-8 15608210 - Nucleic Acids Res. 2005 Jan 1;33(Database issue):D334-7 12671006 - Genome Res. 2003 Apr;13(4):703-16 16551664 - Bioinformatics. 2006 May 15;22(10):1282-3 16500941 - Bioinformatics. 2006 May 1;22(9):1122-9 12603019 - Pac Symp Biocomput. 2003;:77-88 17048406 - IEEE/ACM Trans Comput Biol Bioinform. 2004 Jan-Mar;1(1):24-45 11035779 - Proc Natl Acad Sci U S A. 2000 Oct 24;97(22):12079-84 12169541 - Bioinformatics. 2002;18 Suppl 1:S136-44 17626636 - BMC Bioinformatics. 2007;8:250 16749936 - BMC Bioinformatics. 2006;7:280 11731795 - Nat Genet. 2002 Jan;30(1):41-7 16144809 - Bioinformatics. 2005 Oct 15;21(20):3840-5 17007074 - J Bioinform Comput Biol. 2006 Aug;4(4):911-33 12724301 - Bioinformatics. 2003 May 1;19(7):891-2 17932051 - Nucleic Acids Res. 2008 Jan;36(Database issue):D866-70 10802651 - Nat Genet. 2000 May;25(1):25-9 12935334 - J Comput Biol. 2003;10(3-4):373-84 11102521 - Mol Biol Cell. 2000 Dec;11(12):4241-57 16503973 - BMC Bioinformatics. 2006;7:78 10521349 - Science. 1999 Oct 15;286(5439):531-7 12539951 - Novartis Found Symp. 2002;247:91-101; discussion 101-3, 119-28, 244-52 12734014 - Genome Biol. 2003;4(5):R34 18831786 - BMC Genomics. 2008;9 Suppl 2:S20 |
| References_xml | – volume: 11 start-page: 4241 year: 2000 ident: key 20170510120623_B32 article-title: Genomic expression programs in the response of yeast cells to environmental changes publication-title: Mol. Biol. Cell doi: 10.1091/mbc.11.12.4241 – volume: 4 start-page: 275 year: 2006 ident: key 20170510120623_B16 article-title: Symmetric and asymmetric multi-modality biclustering analysis for microarray data matrix publication-title: J. Bioinform. Comput. Biol. doi: 10.1142/S0219720006002065 – volume: 23 start-page: 50 year: 2007 ident: key 20170510120623_B22 article-title: Computing the maximum similarity bi-clusters of gene expression data publication-title: Bioinformatics doi: 10.1093/bioinformatics/btl560 – volume: 18 start-page: 536 year: 2002 ident: key 20170510120623_B4 publication-title: Clustering gene expression data using a graph-theoretic approach: an application of minimum spanning trees. – volume: 8 start-page: 250 year: 2007 ident: key 20170510120623_B21 article-title: Nearest Neighbor Networks: clustering expression data based on gene neighborhoods publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-8-250 – volume: 33 start-page: D334 year: 2005 ident: key 20170510120623_B30 article-title: EcoCyc: a comprehensive database resource for Escherichia coli publication-title: Nucleic Acids Res. doi: 10.1093/nar/gki108 – volume: 7 start-page: 280 year: 2006 ident: key 20170510120623_B11 article-title: Integrated biclustering of heterogeneous genome-wide datasets for the inference of global regulatory networks publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-7-280 – volume: 10 start-page: 373 year: 2003 ident: key 20170510120623_B12 article-title: Discovering local structure in gene expression data: the order-preserving submatrix problem publication-title: J. Comput. Biol. doi: 10.1089/10665270360688075 – volume: 18 start-page: S136 year: 2002 ident: key 20170510120623_B14 article-title: Discovering statistically significant biclusters in gene expression data publication-title: Bioinformatics doi: 10.1093/bioinformatics/18.suppl_1.S136 – volume: 247 start-page: 91 year: 2002 ident: key 20170510120623_B29 article-title: The KEGG database publication-title: Novartis Found. Symp. doi: 10.1002/0470857897.ch8 – volume: 95 start-page: 14863 year: 1998 ident: key 20170510120623_B3 article-title: Cluster analysis and display of genome-wide expression patterns publication-title: Proc. Natl Acad. Sci. USA doi: 10.1073/pnas.95.25.14863 – volume: 1 start-page: 24 year: 2004 ident: key 20170510120623_B23 article-title: Biclustering algorithms for biological data analysis: a survey publication-title: IEEE/ACM Trans. Comput. Biol. Bioinform. doi: 10.1109/TCBB.2004.2 – volume: 21 start-page: 3840 year: 2005 ident: key 20170510120623_B15 article-title: Shifting and scaling patterns from gene expression data publication-title: Bioinformatics doi: 10.1093/bioinformatics/bti641 – volume: 25 start-page: 25 year: 2000 ident: key 20170510120623_B28 article-title: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium publication-title: Nat Genet. doi: 10.1038/75556 – volume: 22 start-page: 1282 year: 2006 ident: key 20170510120623_B25 article-title: BicAT: a biclustering analysis toolbox publication-title: Bioinformatics doi: 10.1093/bioinformatics/btl099 – volume: 6 start-page: 232 year: 2005 ident: key 20170510120623_B26 article-title: EXPANDER—an integrative program suite for microarray data analysis publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-6-232 – volume: 22 start-page: 1122 year: 2006 ident: key 20170510120623_B13 article-title: A systematic comparison and evaluation of biclustering methods for gene expression data publication-title: Bioinformatics doi: 10.1093/bioinformatics/btl060 – volume: 97 start-page: 12079 year: 2000 ident: key 20170510120623_B7 article-title: Coupled two-way clustering analysis of gene microarray data publication-title: Proc. Natl Acad. Sci. USA doi: 10.1073/pnas.210134797 – volume: 20 start-page: 1993 year: 2004 ident: key 20170510120623_B20 article-title: Defining transcription modules using large-scale gene expression data publication-title: Bioinformatics doi: 10.1093/bioinformatics/bth166 – volume: 32 start-page: 5539 year: 2004 ident: key 20170510120623_B33 article-title: The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkh894 – volume: 18 start-page: 413 year: 2002 ident: key 20170510120623_B2 article-title: A mixture model-based approach to the clustering of microarray expression data publication-title: Bioinformatics doi: 10.1093/bioinformatics/18.3.413 – volume: 58 start-page: 415 year: 1963 ident: key 20170510120623_B5 article-title: Problems in the analysis of survey data, and proposal publication-title: J. Am. Stat. Assoc. doi: 10.1080/01621459.1963.10500855 – volume: 10 start-page: 519 year: 2006 ident: key 20170510120623_B10 article-title: Application of simulated annealing to the biclustering of gene expression data publication-title: IEEE Trans. Inf. Technol. Biomed. doi: 10.1109/TITB.2006.872073 – volume: 30 start-page: 41 year: 2002 ident: key 20170510120623_B35 article-title: MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia publication-title: Nat. Genet. doi: 10.1038/ng765 – volume: 10 start-page: 519 year: 2006 ident: key 20170510120623_B19 article-title: Application of simulated annealing to the biclustering of gene expression data publication-title: IEEE Trans. Inf. Technol. Biomed. doi: 10.1109/TITB.2006.872073 – volume: 13 start-page: 703 year: 2003 ident: key 20170510120623_B34 article-title: Spectral biclustering of microarray data: coclustering genes and conditions publication-title: Genome Res. doi: 10.1101/gr.648603 – volume: 8 start-page: 93 year: 2000 ident: key 20170510120623_B8 article-title: Biclustering of expression data publication-title: Proc. Int. Conf. Intell. Syst. Mol. Biol. – volume: 286 start-page: 531 year: 1999 ident: key 20170510120623_B36 article-title: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring publication-title: Science doi: 10.1126/science.286.5439.531 – volume: 4 start-page: R34 year: 2003 ident: key 20170510120623_B1 article-title: Clustering gene-expression data with repeated measurements publication-title: Genome Biol. doi: 10.1186/gb-2003-4-5-r34 – volume: 67 start-page: 123 year: 1972 ident: key 20170510120623_B6 article-title: Diret clustering of a data matrix publication-title: J. Am. Stat. Assoc. doi: 10.1080/01621459.1972.10481214 – volume: 9 start-page: S20 year: 2008 ident: key 20170510120623_B9 article-title: Extending bicluster analysis to annotate unclassified ORFs and predict novel functional modules using expression data publication-title: BMC Genomics doi: 10.1186/1471-2164-9-S2-S20 – volume: 4 start-page: 911 year: 2006 ident: key 20170510120623_B17 article-title: A general framework for biclustering gene expression data publication-title: J. Bioinform. Comput. Biol. doi: 10.1142/S021972000600217X – volume: 7 start-page: 78 year: 2006 ident: key 20170510120623_B18 article-title: Biclustering of gene expression data by non-smooth non-negative matrix factorization publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-7-78 – volume: 8 start-page: 77 year: 2003 ident: key 20170510120623_B24 article-title: Extracting conserved gene expression motifs from gene expression data publication-title: Pac. Symp. Biocomput. – volume: 36 start-page: D866 year: 2008 ident: key 20170510120623_B27 article-title: Many Microbe Microarrays Database: uniformly normalized Affymetrix compendia with structured experimental metadata publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkm815 – volume: 19 start-page: 891 year: 2003 ident: key 20170510120623_B31 article-title: GeneMerge—post-genomic analysis, data mining, and hypothesis testing publication-title: Bioinformatics doi: 10.1093/bioinformatics/btg114 – reference: 17007074 - J Bioinform Comput Biol. 2006 Aug;4(4):911-33 – reference: 16871720 - IEEE Trans Inf Technol Biomed. 2006 Jul;10(3):519-25 – reference: 16500941 - Bioinformatics. 2006 May 1;22(9):1122-9 – reference: 15486203 - Nucleic Acids Res. 2004;32(18):5539-45 – reference: 16819784 - J Bioinform Comput Biol. 2006 Apr;4(2):275-98 – reference: 17932051 - Nucleic Acids Res. 2008 Jan;36(Database issue):D866-70 – reference: 11934740 - Bioinformatics. 2002 Mar;18(3):413-22 – reference: 16144809 - Bioinformatics. 2005 Oct 15;21(20):3840-5 – reference: 10977070 - Proc Int Conf Intell Syst Mol Biol. 2000;8:93-103 – reference: 18831786 - BMC Genomics. 2008;9 Suppl 2:S20 – reference: 16176576 - BMC Bioinformatics. 2005;6:232 – reference: 11731795 - Nat Genet. 2002 Jan;30(1):41-7 – reference: 12016051 - Bioinformatics. 2002 Apr;18(4):536-45 – reference: 17626636 - BMC Bioinformatics. 2007;8:250 – reference: 17090578 - Bioinformatics. 2007 Jan 1;23(1):50-6 – reference: 12169541 - Bioinformatics. 2002;18 Suppl 1:S136-44 – reference: 12603019 - Pac Symp Biocomput. 2003;:77-88 – reference: 16749936 - BMC Bioinformatics. 2006;7:280 – reference: 12671006 - Genome Res. 2003 Apr;13(4):703-16 – reference: 12935334 - J Comput Biol. 2003;10(3-4):373-84 – reference: 17048406 - IEEE/ACM Trans Comput Biol Bioinform. 2004 Jan-Mar;1(1):24-45 – reference: 16551664 - Bioinformatics. 2006 May 15;22(10):1282-3 – reference: 10802651 - Nat Genet. 2000 May;25(1):25-9 – reference: 15608210 - Nucleic Acids Res. 2005 Jan 1;33(Database issue):D334-7 – reference: 12539951 - Novartis Found Symp. 2002;247:91-101; discussion 101-3, 119-28, 244-52 – reference: 9843981 - Proc Natl Acad Sci U S A. 1998 Dec 8;95(25):14863-8 – reference: 10521349 - Science. 1999 Oct 15;286(5439):531-7 – reference: 12724301 - Bioinformatics. 2003 May 1;19(7):891-2 – reference: 16503973 - BMC Bioinformatics. 2006;7:78 – reference: 12734014 - Genome Biol. 2003;4(5):R34 – reference: 11035779 - Proc Natl Acad Sci U S A. 2000 Oct 24;97(22):12079-84 – reference: 11102521 - Mol Biol Cell. 2000 Dec;11(12):4241-57 – reference: 15044247 - Bioinformatics. 2004 Sep 1;20(13):1993-2003 |
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