1. Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network.
- Author
-
Imoto S, Sunyong K, Goto T, Aburatani S, Tashiro K, Kuhara S, and Miyano S
- Subjects
- Artificial Intelligence, Bayes Theorem, Models, Statistical, Nonlinear Dynamics, Regression Analysis, Saccharomyces cerevisiae Proteins genetics, Stochastic Processes, Transcription Factors genetics, Gene Expression Profiling methods, Gene Expression Regulation physiology, Models, Genetic, Oligonucleotide Array Sequence Analysis methods, Saccharomyces cerevisiae Proteins metabolism, Signal Transduction physiology, Transcription Factors metabolism
- Abstract
We propose a new statistical method for constructing 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.
- Published
- 2002