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

  • Vinh N
  • Chetty M
  • Coppel R
 et al. 
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Abstract

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.

Author-supplied keywords

  • *Algorithms
  • *Gene Expression
  • *Gene Regulatory Networks
  • *Metabolic Networks and Pathways
  • Bayes Theorem
  • Gene Expression Regulation
  • Information Storage and Retrieval
  • Models, Biological
  • Oligonucleotide Array Sequence Analysis/methods
  • Software

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Authors

  • N X Vinh

  • M Chetty

  • R Coppel

  • P P Wangikar

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