Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data.
We propose a dynamic Bayesian network and nonparametric regression model for constructing a gene network from time series microarray gene expression data. The proposed method can overcome a shortcoming of the Bayesian network model in the sense of the construction of cyclic regulations. The proposed method can analyze the microarray data as a continuous data and can capture even nonlinear relations among genes. It can be expected that this model will give a deeper insight into complicated biological systems. We also derive a new criterion for evaluating an estimated network from Bayes approach. We conduct Monte Carlo experiments to examine the effectiveness of the proposed method. We also demonstrate the proposed method through the analysis of the Saccharomyces cerevisiae gene expression data.
Kim
Sunyong
S
Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato, Tokyo 108-8639, Japan.
Imoto
Seiya
S
Miyano
Satoru
S
D001499:Bayes Theorem; D003198:Computer Simulation; D015870:Gene Expression; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D012044:Regression Analysis; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D049490:Systems Biology / Q000379:methods*