title: GlobalMIT: learning globally optimal dynamic bayesian network with the mutual information test criterion.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  The toolbox, implemented in Matlab and C++, is available at http://code.google.com/p/globalmit.
  
  
  BACKGROUND
  vinh.nguyen@monash.edu; madhu.chetty@monash.edu
  
  
  BACKGROUND
  Supplementary data is available at Bioinformatics online.
authors.affiliation: Gippsland School of Information Technology, Faculty of IT, Monash University, Victoria, Australia. vinh.nguyen@monash.edu
authors.forename: Nguyen Xuan
authors.initials: NX
authors.lastname: Vinh
authors.forename: Madhu
authors.initials: M
authors.lastname: Chetty
authors.forename: Ross
authors.initials: R
authors.lastname: Coppel
authors.forename: Pramod P
authors.initials: PP
authors.lastname: Wangikar
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D015870:Gene Expression; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D016247:Information Storage and Retrieval; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D012984:Software