Motivation: Dynamic Bayesian networks (DBN) are widely applied in modeling various biological networks including the gene regulatory network (GRN). Due to the NP-hard nature of learning static Bayesian network structure, most methods for learning DBN also employ either local search such as hill climbing, or a meta stochastic global optimization framework such as genetic algorithm or simulated annealing. Results: This article presents GlobalMIT, a toolbox for learning the globally optimal DBN structure from gene expression data. We propose using a recently introduced information theoretic-based scoring metric named mutual information test (MIT). With MIT, the task of learning the globally optimal DBN is efficiently achieved in polynomial time. © The Author 2011. Published by Oxford University Press. All rights reserved.
CITATION STYLE
Vinh, N. X., Chetty, M., Coppel, R., & Wangikar, P. P. (2011). Globalmit: Learning globally optimal dynamic bayesian network with the mutual information test criterion. Bioinformatics, 27(19), 2765–2766. https://doi.org/10.1093/bioinformatics/btr457
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