Data discretization for dynamic bayesian network based modeling of genetic networks
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
mDBN: motif based learning of gene regulatory networks using dynamic bayesian networks
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Inferring large scale genetic networks with S-system model
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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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. Availability: The toolbox, implemented in Matlab and C++, is available at http://code.google.com/p/globalmit. Contact:vinh.nguyen@monash.edu; madhu.chetty@monash.edu Supplementary information:Supplementary data is available at Bioinformatics online.