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.
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References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Wang, Y. X. Rachel; Li, Lexin; Li, Jingyi Jessica; Huang, Haiyan: Network modeling in biology: statistical methods for gene and brain networks (2021)
- Ajmal, Hamda B.; Madden, Michael G.: Inferring dynamic gene regulatory networks with low-order conditional independencies -- an evaluation of the method (2020)
- Alsanie, Waleed; Cussens, James: Learning failure-free PRISM programs (2015)