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Herein, we propose a method for discovering global changes within a cell by associating observed conditions of gene expression with gene functions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>To elucidate the association, we introduce a novel feature selection method called<jats:italic>Least-Squares Mutual Information (LSMI)<\/jats:italic>, which computes mutual information without density estimaion, and therefore LSMI can detect nonlinear associations within a cell. We demonstrate the effectiveness of LSMI through comparison with existing methods. The results of the application to yeast microarray datasets reveal that non-natural stimuli affect various biological processes, whereas others are no significant relation to specific cell processes. Furthermore, we discover that biological processes can be categorized into four types according to the responses of various stimuli: DNA\/RNA metabolism, gene expression, protein metabolism, and protein localization.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>We proposed a novel feature selection method called LSMI, and applied LSMI to mining the association between conditions of yeast and biological processes through microarray datasets. In fact, LSMI allows us to elucidate the global organization of cellular process control.<\/jats:p><\/jats:sec>","DOI":"10.1186\/1471-2105-10-s1-s52","type":"journal-article","created":{"date-parts":[[2009,1,30]],"date-time":"2009-01-30T20:05:15Z","timestamp":1233345915000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":68,"title":["Mutual information estimation reveals global associations between stimuli and biological processes"],"prefix":"10.1186","volume":"10","author":[{"given":"Taiji","family":"Suzuki","sequence":"first","affiliation":[]},{"given":"Masashi","family":"Sugiyama","sequence":"additional","affiliation":[]},{"given":"Takafumi","family":"Kanamori","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Sese","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2009,1,30]]},"reference":[{"key":"3235_CR1","doi-asserted-by":"publisher","first-page":"3710","DOI":"10.1093\/bioinformatics\/bth456","volume":"20","author":"EI Boyle","year":"2004","unstructured":"Boyle EI, Weng S, Gollub J, Jin H, Botstein D, Cherry JM, Sherlock G: GO::TermFinder \u2013 open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes. bioinformatics. 2004, 20: 3710-3715. 10.1093\/bioinformatics\/bth456.","journal-title":"bioinformatics"},{"key":"3235_CR2","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1186\/1471-2105-8-111","volume":"8","author":"I Priness","year":"2007","unstructured":"Priness I, Maimon O, Ben-Gal I: Evaluation of gene-expression clustering via mutual information distance measure. 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