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

• Published in: Bioinformatics Vol. 27; no. 19; pp. 2765 - 2766
• Main Authors: Vinh, Nguyen Xuan, Chetty, Madhu, Coppel, Ross, Wangikar, Pramod P.
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 01.10.2011
• Subjects: Algorithms; Bayes Theorem; Biological and medical sciences; Fundamental and applied biological sciences. Psychology; Gene Expression; Gene Expression Regulation; Gene Regulatory Networks; General aspects; Information Storage and Retrieval; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Metabolic Networks and Pathways; Models, Biological; Oligonucleotide Array Sequence Analysis - methods; Software; Learning; Acquisition process; Criterion; Dynamics; Test; Information network; Bayes network
• ISSN: 1367-4803; 1367-4811; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btr457

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.