Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network

Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network (2002)

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by Seiya Imoto , Kim Sunyong , Takao Goto , Sachiyo Aburatani , Kousuke Tashiro , Satoru Kuhara , Satoru Miyano

Venue:	Proc. 1st IEEE Computer Society Bioinformatics Conference
Citations:	27 - 16 self

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Metadata Version 1

Datum	Value	Source
TITLE	Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network	INFERENCE
AUTHOR NAME	Seiya Imoto	SVM HeaderParse 0.2
AUTHOR AFFIL	1 Human Genome Center, Institute of Medical Science, University of Tokyo	SVM HeaderParse 0.2
AUTHOR ADDR	4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan	SVM HeaderParse 0.2
AUTHOR NAME	Kim Sunyong	SVM HeaderParse 0.2
AUTHOR AFFIL	1 Human Genome Center, Institute of Medical Science, University of Tokyo	SVM HeaderParse 0.2
AUTHOR ADDR	4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan	SVM HeaderParse 0.2
AUTHOR NAME	Takao Goto	SVM HeaderParse 0.2
AUTHOR AFFIL	1 Human Genome Center, Institute of Medical Science, University of Tokyo	SVM HeaderParse 0.2
AUTHOR ADDR	4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan	SVM HeaderParse 0.2
AUTHOR NAME	Sachiyo Aburatani	SVM HeaderParse 0.2
AUTHOR AFFIL	2 Graduate School of Genetic Resources Technology, Kyushu University	SVM HeaderParse 0.2
AUTHOR ADDR	6-10-1 Hakozaki, Higashi-ku, Fukuoka, 812-8581, Japan	SVM HeaderParse 0.2
AUTHOR NAME	Kousuke Tashiro	SVM HeaderParse 0.2
AUTHOR AFFIL	2 Graduate School of Genetic Resources Technology, Kyushu University	SVM HeaderParse 0.2
AUTHOR ADDR	6-10-1 Hakozaki, Higashi-ku, Fukuoka, 812-8581, Japan	SVM HeaderParse 0.2
AUTHOR NAME	Satoru Kuhara	SVM HeaderParse 0.2
AUTHOR AFFIL	2 Graduate School of Genetic Resources Technology, Kyushu University	SVM HeaderParse 0.2
AUTHOR ADDR	6-10-1 Hakozaki, Higashi-ku, Fukuoka, 812-8581, Japan	SVM HeaderParse 0.2
AUTHOR NAME	Satoru Miyano	SVM HeaderParse 0.2
AUTHOR AFFIL	1 Human Genome Center, Institute of Medical Science, University of Tokyo	SVM HeaderParse 0.2
AUTHOR ADDR	4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan	SVM HeaderParse 0.2
ABSTRACT	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 in 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. A problem still remains to be solved in selecting an optimal graph, which gives the best representation of the system among genes. 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. 1.	SVM HeaderParse 0.2
YEAR	2002	INFERENCE
VENUE	Proc. 1st IEEE Computer Society Bioinformatics Conference	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	219--227	INFERENCE
VOLUME	1	INFERENCE
CITATIONS	31 found	ParsCit 1.0