Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network.
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.
Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan. imoto@ims.u-tokyo.ac.jp
Seiya
S
Imoto
Sunyong
S
Kim
Takao
T
Goto
Satoru
S
Miyano
Sachiyo
S
Aburatani
Kousuke
K
Tashiro
Satoru
S
Kuhara
D001499:Bayes Theorem; D020869:Gene Expression Profiling / Q000379:methods*; D015966:Gene Expression Regulation, Fungal / Q000502:physiology*; D008957:Models, Genetic; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012044:Regression Analysis; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics* / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
D029701:Saccharomyces cerevisiae Proteins