A Biclustering Method for Gene Expression Module Discovery Using a Closed Itemset Enumeration Algorithm

• Item type: Trans(1)
• 公開日: 2007-03-15
• タイトル: A Biclustering Method for Gene Expression Module Discovery Using a Closed Itemset Enumeration Algorithm
• タイトル / 言語: en
• タイトル: A Biclustering Method for Gene Expression Module Discovery Using a Closed Itemset Enumeration Algorithm
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: Original Papers
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Computational Biology Research Center (CBRC) National Institute of Advanced Industrial Science and Technology (AIST)
• 著者所属: Computational Biology Research Center (CBRC) National Institute of Advanced Industrial Science and Technology (AIST)
• 著者所属: Computational Bio
• 著者所属(英): en
• 著者所属(英): Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST)
• 著者所属(英): en
• 著者所属(英): Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST)
• 著者所属(英): en
• 著者所属(英): Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST)
• 著者名: Yoshifumi, Okada; Wataru, Fujibuchi; Paul, Horton
• 著者名(英): Yoshifumi, Okada; Wataru, Fujibuchi; Paul, Horton
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: A gene expression module (module for short) is a set of genes with shared expression behavior under certain experimental conditions. Discovering of modules enables us to uncover the function of uncharacterized genes or genetic networks. In recent years several biclustering methods have been suggested to discover modules from gene expression data matrices where a bicluster is defined as a subset of genes that exhibit a highly correlated expression pattern over a subset of conditions. Biclustering however involves combinatorial optimization in selecting the rows and columns composing modules. Hence most existing algorithms are based on heuristic or stochastic approaches and produce possibly sub-optimal solutions. In this paper we propose a novel biclustering method BiModule based on a closed itemset enumeration algorithm. By exhaustive enumeration of such biclusters it is possible to select only biclusters satisfying certain criteria such as a user-specified bicluster size an enrichment of functional annotation terms etc. We performed comparative experiments to existing salient biclustering methods to test the validity of biclusters extracted by BiModule using synthetic data and real expression data. We show that BiModule provides high performance compared to the other methods in extracting artificially-embedded modules as well as modules strongly related to GO annotations protein-protein interactions and metabolic pathways.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: A gene expression module (module for short) is a set of genes with shared expression behavior under certain experimental conditions. Discovering of modules enables us to uncover the function of uncharacterized genes or genetic networks. In recent years, several biclustering methods have been suggested to discover modules from gene expression data matrices, where a bicluster is defined as a subset of genes that exhibit a highly correlated expression pattern over a subset of conditions. Biclustering however involves combinatorial optimization in selecting the rows and columns composing modules. Hence most existing algorithms are based on heuristic or stochastic approaches and produce possibly sub-optimal solutions. In this paper, we propose a novel biclustering method, BiModule, based on a closed itemset enumeration algorithm. By exhaustive enumeration of such biclusters, it is possible to select only biclusters satisfying certain criteria such as a user-specified bicluster size, an enrichment of functional annotation terms, etc. We performed comparative experiments to existing salient biclustering methods to test the validity of biclusters extracted by BiModule using synthetic data and real expression data. We show that BiModule provides high performance compared to the other methods in extracting artificially-embedded modules as well as modules strongly related to GO annotations, protein-protein interactions and metabolic pathways.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA12177013
• 書誌情報: IPSJ Transactions on Bioinformatics （TBIO）; 巻 48, 号 SIG5(TBIO2), p. 39-48, 発行日 2007-03-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-6679
• 出版者 / 言語: ja
• 出版者: 情報処理学会