Inferring gene networks from time series microarray data using dynamic Bayesian networks

• Published in: Briefings in bioinformatics Vol. 4; no. 3; pp. 228 - 235
• Main Author: Kim, S. Y.
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 01.09.2003; Oxford Publishing Limited (England)
• Subjects: Algorithms; Bayes Theorem; Bioinformatics; Biological and medical sciences; Computational Biology - methods; Computer Simulation; Fundamental and applied biological sciences. Psychology; Gene Expression Profiling; Gene Expression Regulation, Fungal; General aspects; Genes; Mathematical analysis; Mathematics; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Models, Genetic; Oligonucleotide Array Sequence Analysis; Saccharomyces cerevisiae; Saccharomyces cerevisiae - genetics; Saccharomyces cerevisiae - metabolism; Time Factors; Time series; Fungi; Regulation(control); Yeast; Classification; DNA chip; Bayes network; Ascomycetes; Time measurement; Review; Bioinformatics; Saccharomyces cerevisiae; Thallophyta
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/4.3.228

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.