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

• DOI: 10.1093/bioinformatics/btr457
• Publication Date: 2011-08-04T05:11:18Z
• Authors: Nguyen Xuan Vinh; Madhu Chetty; Ross Coppel; Pramod P. Wangikar
• Keywords: 0301 basic medicine; 03 medical and health sciences; Gene Expression Regulation; Gene Expression; Information Storage and Retrieval; Bayes Theorem; Gene Regulatory Networks; Models, Biological; Algorithms; Metabolic Networks and Pathways; Software; Oligonucleotide Array Sequence Analysis

AbstractMotivation: 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: [e-mail]; [e-mail] information: Supplementary data is available at Bioinformatics online.