Inferring gene networks from time series microarray data using dynamic Bayesian networks
Graduate student at the laboratory of DNA analysis, Human Genome Centre, Institute of Medical Science, University of Tokyo, Japan. Her current interests include modelling gene networks from time series microarray gene expression data using statistical methods, including DBNs.
Currently a research associate at the laboratory of DNA analysis, Human Genome Centre, Institute of Medical Science, University of Tokyo. His current research interests cover analysis of high-dimensional data with complex structure using nonlinear statistical methods, as well as development model selection criteria from an information theoretic or a Bayesian statistics approach, and analysis of cDNA microarray gene expression data.
Professor at the Human Genome Centre, Institute of Medical Science, University of Tokyo. His current interests include computational gene network inference methods, modelling and simulation of biological systems, and computational knowledge discovery. He is on the Editorial Board of Bioinformatics, J. Bioinformatics and Computational Biology and Theoretical Computer Science, and is the Chief Editor of Genome Informatics.
SunYong Kim, Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan Tel: +81 3 5449 5615 Fax: +81 3 5449 5442 E-mail: sunk{at}ims.u-tokyo.ac.jp
Dynamic Bayesian networks (DBNs) are considered as a promising model for inferring gene networks from time series microarray data. DBNs have overtaken Bayesian networks (BNs) as DBNs can construct cyclic regulations using time delay information. In this paper, a general framework for DBN modelling is outlined. Both discrete and continuous DBN models are constructed systematically and criteria for learning network structures are introduced from a Bayesian statistical viewpoint. This paper reviews the applications of DBNs over the past years. Real data applications for Saccharomyces cerevisiae time series gene expression data are also shown.
Keywords: microarray, gene networks, DBNs
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