{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T04:07:51Z","timestamp":1790914071299,"version":"4.1.0"},"reference-count":39,"publisher":"World Scientific Pub Co Pte Ltd","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Bioinform. Comput. Biol."],"published-print":{"date-parts":[[2003,7]]},"abstract":"<jats:p>We propose a new statistical method for constructing a genetic network from microarray gene expression data by using a Bayesian network. An essential point of Bayesian network construction is the estimation of the conditional distribution of each random variable. We consider fitting nonparametric regression models with heterogeneous error variances to the microarray gene expression data to capture the nonlinear structures between genes. Selecting the optimal graph, which gives the best representation of the system among genes, is still a problem to be solved. We theoretically derive a new graph selection criterion from Bayes approach in general situations. The proposed method includes previous methods based on Bayesian networks. We demonstrate the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae gene expression data newly obtained by disrupting 100 genes.<\/jats:p>","DOI":"10.1142\/s0219720003000071","type":"journal-article","created":{"date-parts":[[2003,7,21]],"date-time":"2003-07-21T06:06:17Z","timestamp":1058767577000},"page":"231-252","source":"Crossref","is-referenced-by-count":88,"title":["Bayesian Network and Nonparametric Heteroscedastic Regression for  Nonlinear Modeling of Genetic Network"],"prefix":"10.1142","volume":"01","author":[{"given":"Seiya","family":"Imoto","sequence":"first","affiliation":[{"name":"Human Genome Center,  Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku,  Tokyo 108-8639, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sunyong","family":"Kim","sequence":"additional","affiliation":[{"name":"Human Genome Center,  Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku,  Tokyo 108-8639, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takao","family":"Goto","sequence":"additional","affiliation":[{"name":"Human Genome Center,  Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku,  Tokyo 108-8639, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sachiyo","family":"Aburatani","sequence":"additional","affiliation":[{"name":"Graduate School of Genetic  Resources Technology, Kyushu University, 6-10-1  Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kousuke","family":"Tashiro","sequence":"additional","affiliation":[{"name":"Graduate School of Genetic  Resources Technology, Kyushu University, 6-10-1  Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Satoru","family":"Kuhara","sequence":"additional","affiliation":[{"name":"Graduate School of Genetic  Resources Technology, Kyushu University, 6-10-1  Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Satoru","family":"Miyano","sequence":"additional","affiliation":[{"name":"Human Genome Center,  Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku,  Tokyo 108-8639, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"219","published-online":{"date-parts":[[2012,1,25]]},"reference":[{"key":"rf2","unstructured":"H.\u00a0Akaike, Bayesian Statistics, eds. 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