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Volumn 27, Issue 19, 2011, Pages 2765-2766

Globalmit: Learning globally optimal dynamic bayesian network with the mutual information test criterion

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; BAYES THEOREM; BIOLOGICAL MODEL; COMPUTER PROGRAM; DNA MICROARRAY; GENE EXPRESSION; GENE EXPRESSION REGULATION; GENE REGULATORY NETWORK; INFORMATION RETRIEVAL; METABOLISM; METHODOLOGY;

EID: 80053452578     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btr457     Document Type: Article
Times cited : (72)

References (13)
  • 2
    • 33750071718 scopus 로고    scopus 로고
    • A scoring function for learning bayesian networks based on mutual information and conditional independence tests
    • de Campos, L.M. (2006) A scoring function for learning bayesian networks based on mutual information and conditional independence tests. J. Mach. Learn. Res., 7, 2149-2187.
    • (2006) J. Mach. Learn. Res. , vol.7 , pp. 2149-2187
    • de Campos, L.M.1
  • 4
    • 0344464762 scopus 로고    scopus 로고
    • Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks
    • Husmeier, D. (2003) Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinformatics, 19, 2271-2282.
    • (2003) Bioinformatics , vol.19 , pp. 2271-2282
    • Husmeier, D.1
  • 5
    • 0842309206 scopus 로고    scopus 로고
    • Inferring gene networks from time series microarray data using dynamic Bayesian networks
    • Kim, S.Y. et al. (2003) Inferring gene networks from time series microarray data using dynamic Bayesian networks. Brief. Bioinformat., 4, 228-235.
    • (2003) Brief. Bioinformat. , vol.4 , pp. 228-235
    • Kim, S.Y.1
  • 6
    • 0004158155 scopus 로고    scopus 로고
    • Modelling gene expression data using dynamic bayesian networks
    • Computer Science Division, University of California, Berkeley, CA
    • Murphy, K.P. and Mian, S. (1999) Modelling gene expression data using dynamic bayesian networks. Technical Report, Computer Science Division, University of California, Berkeley, CA.
    • (1999) Technical Report
    • Murphy, K.P.1    Mian, S.2
  • 7
    • 4143058645 scopus 로고    scopus 로고
    • Gene networks inference using dynamic Bayesian networks
    • Perrin, B.-E. et al. (2003) Gene networks inference using dynamic Bayesian networks. Bioinformatics, 19 (Suppl. 2), ii138-ii148.
    • (2003) Bioinformatics , vol.19 , Issue.SUPPL. 2
    • Perrin, B.-E.1
  • 8
    • 77955124773 scopus 로고    scopus 로고
    • Learning Bayesian networks with the bnlearn R package
    • Scutari, M. (2010) Learning Bayesian networks with the bnlearn R package. J. Stat. Soft., 35, 1-22.
    • (2010) J. Stat. Soft. , vol.35 , pp. 1-22
    • Scutari, M.1
  • 9
    • 33751407959 scopus 로고    scopus 로고
    • Computational inference of neural information flow networks
    • Smith, V.A. et al. (2006) Computational inference of neural information flow networks. PLoS Comput. Biol., 2, e161.
    • (2006) PLoS Comput. Biol. , vol.2
    • Smith, V.A.1
  • 10
    • 80053453613 scopus 로고    scopus 로고
    • A polynomial time algorithm for learning globally optimal dynamic Bayesian network and its applications in genetic network reconstruction
    • Faculty of IT, Monash University
    • Vinh, N.X. et al. (2011) A polynomial time algorithm for learning globally optimal dynamic Bayesian network and its applications in genetic network reconstruction. Technical Report FIT-GSIT TR.1101, Faculty of IT, Monash University.
    • (2011) Technical Report FIT-GSIT TR.1101
    • Vinh, N.X.1
  • 11
    • 58349093534 scopus 로고    scopus 로고
    • BNFinder: exact and efficient method for learning Bayesian networks
    • Wilczynski, B. and Dojer, N. (2009) BNFinder: exact and efficient method for learning Bayesian networks. Bioinformatics, 25, 286-287.
    • (2009) Bioinformatics , vol.25 , pp. 286-287
    • Wilczynski, B.1    Dojer, N.2
  • 12
    • 12344259602 scopus 로고    scopus 로고
    • Advances to Bayesian network inference for generating causal networks from observational biological data
    • Yu, J. et al. (2004) Advances to Bayesian network inference for generating causal networks from observational biological data. Bioinformatics, 20, 3594-3603.
    • (2004) Bioinformatics , vol.20 , pp. 3594-3603
    • Yu, J.1
  • 13
    • 12744261506 scopus 로고    scopus 로고
    • A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data
    • Zou, M. and Conzen, S.D. (2005) A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics, 21, 71-79.
    • (2005) Bioinformatics , vol.21 , pp. 71-79
    • Zou, M.1    Conzen, S.D.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.