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We investigate in this paper reverse engineering of gene regulatory networks from time-series microarray data. We apply dynamic Bayesian networks (DBNs) for modeling cell cycle regulations. In developing a network inference algorithm, we focus on soft solutions that can provide a posteriori probability (APP) of network topology. In particular, we propose a variational Bayesian structural expectation maximization algorithm that can learn the posterior distribution of the network model parametersdoi:10.1155/2007/71312 pmid:18309364 pmcid:PMC3171349 fatcat:zcbth2n2qfekjo3drl5jkniavm