Combining microarrays and biological knowledge for estimating gene networks via bayesian networks.
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. 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.
Imoto
Seiya
S
Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan. imoto@ims.u-tokyo.ac.jp
Higuchi
Tomoyuki
T
Goto
Takao
T
Tashiro
Kousuke
K
Kuhara
Satoru
S
Miyano
Satoru
S
D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D016208:Databases, Factual; D015966:Gene Expression Regulation, Fungal / Q000502:physiology*; D016247:Information Storage and Retrieval / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D029701:Saccharomyces cerevisiae Proteins / Q000502:physiology*; D015398:Signal Transduction / Q000502:physiology*
D029701:Saccharomyces cerevisiae Proteins