{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T12:33:44Z","timestamp":1767962024469,"version":"3.49.0"},"reference-count":13,"publisher":"Oxford University Press (OUP)","issue":"19","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2011,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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.<\/jats:p><jats:p>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.<\/jats:p><jats:p>Availability: The toolbox, implemented in Matlab and C++, is available at http:\/\/code.google.com\/p\/globalmit.<\/jats:p><jats:p>Contact: \u00a0vinh.nguyen@monash.edu; madhu.chetty@monash.edu<\/jats:p><jats:p>Supplementary information: \u00a0Supplementary data is available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btr457","type":"journal-article","created":{"date-parts":[[2011,8,4]],"date-time":"2011-08-04T05:11:18Z","timestamp":1312434678000},"page":"2765-2766","source":"Crossref","is-referenced-by-count":61,"title":["GlobalMIT: learning globally optimal dynamic bayesian network with the mutual information test criterion"],"prefix":"10.1093","volume":"27","author":[{"given":"Nguyen Xuan","family":"Vinh","sequence":"first","affiliation":[{"name":"1 Gippsland School of Information Technology, Faculty of IT, Monash University, 2Department of Microbiology, Faculty of Medicine, Nursing and Health Sciences, Monash University, Victoria, Australia and 3Department of Chemical Engineering, Indian Institute of Technology, Bombay, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Madhu","family":"Chetty","sequence":"additional","affiliation":[{"name":"1 Gippsland School of Information Technology, Faculty of IT, Monash University, 2Department of Microbiology, Faculty of Medicine, Nursing and Health Sciences, Monash University, Victoria, Australia and 3Department of Chemical Engineering, Indian Institute of Technology, Bombay, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ross","family":"Coppel","sequence":"additional","affiliation":[{"name":"1 Gippsland School of Information Technology, Faculty of IT, Monash University, 2Department of Microbiology, Faculty of Medicine, Nursing and Health Sciences, Monash University, Victoria, Australia and 3Department of Chemical Engineering, Indian Institute of Technology, Bombay, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pramod P.","family":"Wangikar","sequence":"additional","affiliation":[{"name":"1 Gippsland School of Information Technology, Faculty of IT, Monash University, 2Department of Microbiology, Faculty of Medicine, Nursing and Health Sciences, Monash University, Victoria, Australia and 3Department of Chemical Engineering, Indian Institute of Technology, Bombay, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2011,8,3]]},"reference":[{"key":"2023012512010694600_B1","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1007\/978-1-4612-2404-4_12","article-title":"Learning Bayesian networks is NP-complete","volume-title":"Learning from Data: Artificial Intelligence and Statistics V","author":"Chickering","year":"1996"},{"key":"2023012512010694600_B2","first-page":"2149","article-title":"A scoring function for learning bayesian networks based on mutual information and conditional independence tests","volume":"7","author":"de Campos","year":"2006","journal-title":"J. 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