Inferring gene networks from time series microarray data using dynamic Bayesian networks.
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.
Kim
Sun Yong
SY
Laboratory of DNA analysis, Human Genome Centre, Institute of Medical Science, University of Tokyo, Japan. sunk@ims.u-tokyo.ac.jp
Imoto
Seiya
S
Miyano
Satoru
S
D000465:Algorithms; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D020869:Gene Expression Profiling; D015966:Gene Expression Regulation, Fungal; D008433:Mathematics; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D013997:Time Factors