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

Sun Yong Kim; Seiya Imoto; Satoru Miyano

doi:10.1093/bib/4.3.228

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

Sun Yong Kim·Seiya Imoto·Satoru Miyano

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Abstract

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.

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Algorithms Bayes Theorem Computational Biology Computer Simulation Gene Expression Profiling Gene Expression Regulation, Fungal Mathematics Models, Genetic Oligonucleotide Array Sequence Analysis Saccharomyces cerevisiae Time Factors Journal Article

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BibTeX RIS

Sun Yong Kim, Seiya Imoto, Satoru Miyano. 2003. Inferring gene networks from time series microarray data using dynamic Bayesian networks.. https://doi.org/10.1093/bib%2F4.3.228

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