Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network.
- 1 July 2003
- journal article
- research article
- Published by World Scientific Pub Co Pte Ltd in Journal of Bioinformatics and Computational Biology
- Vol. 1 (2), 231-252
- https://doi.org/10.1142/s0219720003000071
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.Keywords
This publication has 17 references indexed in Scilit:
- Variance stabilization applied to microarray data calibration and to the quantification of differential expressionBioinformatics, 2002
- A variance-stabilizing transformation for gene-expression microarray dataBioinformatics, 2002
- Evaluating functional network inference using simulations of complex biological systemsBioinformatics, 2002
- Using Bayesian Networks to Analyze Expression DataJournal of Computational Biology, 2000
- Algorithms for Identifying Boolean Networks and Related Biological Networks Based on Matrix Multiplication and Fingerprint FunctionJournal of Computational Biology, 2000
- Generalised information criteria in model selectionBiometrika, 1996
- Translation of the yeast transcriptional activator GCN4 is stimulated by purine limitation: implications for activation of the protein kinase GCN2.Molecular and Cellular Biology, 1993
- Coregulation of purine and histidine biosynthesis by the transcriptional activators BAS1 and BAS2.Proceedings of the National Academy of Sciences, 1992
- Approximate predictive likelihoodBiometrika, 1986
- Estimating the Dimension of a ModelThe Annals of Statistics, 1978