Estimating Gene Networks by Bayesian Networks from Microarrays and Biological Knowledge
Seiya Imoto1, Tomoyuki Higuchi2, Takao Goto, Kousuke Tashiro, Satoru Kuhara, Satoru Miyano
1imoto@ims.u-tokyo.ac.jp, University of Tokyo; 2higuchi@ism.ac.jp, The Institute of Statistical Mathematics
We propose a statistical method for estimating a gene network based on Bayesian networks from microarray gene expression data together with biological knowledge including protein-protein interactions, protein-DNA interactions, binding site information, existing literature and so on. Unfortunately, microarray data do not contain enough information for constructing gene networks accurately in many cases. Our method adds biological knowledge to the estimation method of gene networks under a Bayesian statistical framework, and also controls the trade-off between microarray information and biological knowledge automatically. We conduct Monte Carlo simulations to show the effectiveness of the proposed method. We analyze Saccharomyces cerevisiae gene expression data as an application.