Estimation of genetic networks and functional structures between genes by using Bayesian networks and nonparametric regression.
We propose a new method for constructing genetic network from gene expression data by using Bayesian networks. We use nonparametric regression for capturing nonlinear relationships between genes and derive a new criterion for choosing the network in general situations. In a theoretical sense, our proposed theory and methodology include previous methods based on Bayes approach. We applied the proposed method to the S. cerevisiae cell cycle data and showed the effectiveness of our method by comparing with previous methods.
Imoto
Seiya
S
Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan.
Goto
Takao
T
Miyano
Satoru
S
D001499:Bayes Theorem; D005821:Genetic Techniques; D008957:Models, Genetic; D015233:Models, Statistical; D012044:Regression Analysis; D018709:Statistics, Nonparametric