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

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

Research output: Contribution to journalArticle

89 Citations (Scopus)

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 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.

Original languageEnglish
Pages (from-to)231-252
Number of pages22
JournalJournal of bioinformatics and computational biology
Volume1
Issue number2
DOIs
Publication statusPublished - Jul 2003

All Science Journal Classification (ASJC) codes

  • Biochemistry
  • Molecular Biology
  • Computer Science Applications

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