ISSN : 1796-2048
Volume : 2    Issue : 3    Date : June 2007

Bayesian Inference of Genetic Regulatory Networks from Time Series Microarray Data Using
Dynamic Bayesian Networks
Yufei Huang, Jianyin Wang, Jianqiu Zhang, Maribel Sanchez, and Yufeng Wang
Page(s): 46-56
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Reverse engineering of genetic regulatory networks from time series microarray data are
investigated. We propose a dynamic Bayesian networks (DBNs) modeling and a full Bayesian
learning scheme. The proposed DBN directly models the continuous expression levels and also is
associated with parameters that indicate the degree as well as the type of regulations. To learn the
network from data, we proposed a reversible jump Markov chain Monte Carlo (RJMCMC) algorithm.
The RJMCMC algorithm can provide not only more accurate inference results than the deterministic
alternative algorithms but also an estimate of the a posteriori probabilities (APPs) of the network
topology. The estimated APPs provide useful information on the confidence of the inferred results
and can also be used for efficient Bayesian data integration. The proposed approach is tested on
yeast cell cycle microarray data and the results are compared with the KEGG pathway map.

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