title: Gene regulatory network clustering for graph layout based on microarray gene expression data.
abstract: We propose a statistical model realizing simultaneous estimation of gene regulatory network and gene module identification from time series gene expression data from microarray experiments. Under the assumption that genes in the same module are densely connected, the proposed method detects gene modules based on the variational Bayesian technique. The model can also incorporate existing biological prior knowledge such as protein subcellular localization. We apply the proposed model to the time series data from a synthetically generated network and verified the effectiveness of the proposed model. The proposed model is also applied the time series microarray data from HeLa cell. Detected gene module information gives the great help on drawing the estimated gene network.
authors.affiliation: Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan. kaname@ims.u-tokyo.ac.jp.
authors.forename: Kaname
authors.initials: K
authors.lastname: Kojima
authors.forename: Seiya
authors.initials: S
authors.lastname: Imoto
authors.forename: Masao
authors.initials: M
authors.lastname: Nagasaki
authors.forename: Satoru
authors.initials: S
authors.lastname: Miyano
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D006367:HeLa Cells; D006801:Humans; D016014:Linear Models; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011336:Probability