GlobalMIT: learning globally optimal dynamic bayesian network with the mutual information test criterion.

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.