Teppei Shimamura (firstname.lastname@example.org)
Seiya Imoto (email@example.com)
Rui Yamaguchi (firstname.lastname@example.org)
Satoru Miyano (email@example.com)
Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1
Shirokanedai, Minato-ku, Tokyo 108-8639, Japan
We propose a statistical method based on graphical Gaussian models for estimating large gene networks from DNA microarray data. In estimating large gene networks, the number of genes is larger than the number of samples, we need to consider some restrictions for model building. We propose weighted lasso estimation for the graphical Gaussian models as a model of large gene networks. In the proposed method, the structural learning for gene networks is equivalent to the selection of the regularization parameters included in the weighted lasso estimation. We investigate this problem from a Bayes approach and derive an empirical Bayesian information criterion for choosing them. Unlike Bayesian network approach, our method can find the optimal network structure and does not require to use heuristic structural learning algorithm. We conduct Monte Carlo simulation to show the effectiveness of the proposed method. We also analyze Arabidopsis thaliana microarray data and estimate gene networks.