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Volumn 939, Issue , 2013, Pages 35-45

Structure learning for Bayesian networks as models of biological networks

Author keywords

Active learning; Dynamic Bayesian networks; Static Bayesian networks; Structure learning

Indexed keywords


EID: 84871892283     PISSN: 10643745     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-1-62703-107-3_4     Document Type: Article
Times cited : (12)

References (24)
  • 1
    • 0002370418 scopus 로고    scopus 로고
    • A tutorial on learning with Bayesian networks
    • Jordan MI (ed) MIT Press, Cambridgee
    • Heckerman D (1998) A tutorial on learning with Bayesian networks. In: Jordan MI (ed) Learning in graphical models, pp 301-354. MIT Press, Cambridgee
    • (1998) Learning in Graphical Models , pp. 301-354
    • Heckerman, D.1
  • 2
    • 33746898101 scopus 로고    scopus 로고
    • Introduction to learning Bayesian networks from data
    • Husmeier D, Dybowski R, Roberts S (eds) Springer, Berlin
    • Husmeier D (2005) Introduction to learning Bayesian networks from data. In: Husmeier D, Dybowski R, Roberts S (eds) Probabilistic modeling in bioinformatics and medical informatics. Springer, Berlin, pp 17-577
    • (2005) Probabilistic Modeling in Bioinformatics and Medical Informatics , pp. 17-577
    • Husmeier, D.1
  • 3
    • 34249832377 scopus 로고
    • A Bayesian method for the induction of probabilistic networks from data
    • Cooper G, Herskovits E (1992) A Bayesian method for the induction of probabilistic networks from data. Mach Learn 9:309-3477
    • (1992) Mach Learn , vol.9 , pp. 309-3477
    • Cooper, G.1    Herskovits, E.2
  • 7
    • 34249761849 scopus 로고
    • Learning Bayesian networks: The combination of knowledge and statistical data
    • Heckerman D, Geiger D, Chickering D (1995) Learning Bayesian networks: the combination of knowledge and statistical data. Mach Learn 20:197-2433
    • (1995) Mach Learn , vol.20 , pp. 197-2433
    • Heckerman, D.1    Geiger, D.2    Chickering, D.3
  • 10
    • 21844520724 scopus 로고
    • Bayesian graphical models for discrete data
    • Madigan D, York J (1995) Bayesian graphical models for discrete data. Int Stat Rev 63:215-2322
    • (1995) Int Stat Rev , vol.63 , pp. 215-2322
    • Madigan, D.1    York, J.2
  • 11
    • 2542465947 scopus 로고    scopus 로고
    • On inclusion-driven learning of Bayesian networks
    • Castelo R, Kočka T (2003) On inclusion-driven learning of Bayesian networks. J Mach Learn Res 4:527-5744
    • (2003) J Mach Learn Res , vol.4 , pp. 527-5744
    • Castelo, R.1    Kočka, T.2
  • 12
    • 43049097125 scopus 로고    scopus 로고
    • Improving the structure MCMC sampler for Bayesian networks by introducing a new edge reversal move
    • GrzegorczykM, Husmeier D (2008) Improving the structure MCMC sampler for Bayesian networks by introducing a new edge reversal move. Mach Learn 71:265-3055
    • (2008) Mach Learn , vol.71 , pp. 265-3055
    • Grzegorczykm Husmeier, D.1
  • 13
    • 0037262841 scopus 로고    scopus 로고
    • Being Bayesian about network structure. A bayesian approach to structure discovery in Bayesian networks
    • Friedman N, Koller D (2003) Being Bayesian about network structure. A bayesian approach to structure discovery in Bayesian networks. Mach Learn 50:95-1255
    • (2003) Mach Learn , vol.50 , pp. 95-1255
    • Friedman, N.1    Koller, D.2
  • 14
    • 31844439894 scopus 로고    scopus 로고
    • Exact Bayesian structure discovery in Bayesian networks
    • Kovisto M, Sood K (2004) Exact Bayesian structure discovery in Bayesian networks. J Mach Learn Res 5:549-5733
    • (2004) J Mach Learn Res , vol.5 , pp. 549-5733
    • Kovisto, M.1    Sood, K.2
  • 17
    • 43049129993 scopus 로고    scopus 로고
    • Learning the structure of dynamic Bayesian networks from time series and steady state measurements
    • L€ahdesm€aki H, Shmulevich I (2008) Learning the structure of dynamic Bayesian networks from time series and steady state measurements. Mach Learn 71:185-2177
    • (2008) Mach Learn , vol.71 , pp. 185-2177
    • Lahdesmaki, H.1    Shmulevich, I.2
  • 18
    • 10244230983 scopus 로고    scopus 로고
    • Reconstruction of gene networks using Bayesian learning and manipulation experiments
    • Pournara I, Wernisch L (2004) Reconstruction of gene networks using Bayesian learning and manipulation experiments. Bioinformatics 20 (17):2934-29422
    • (2004) Bioinformatics , vol.20 , Issue.17 , pp. 2934-29422
    • Pournara, I.1    Wernisch, L.2
  • 21
    • 17644427718 scopus 로고    scopus 로고
    • Protein-signaling networks derived from multiparameter single-cell data
    • Sachs K, Perez O, Peer DA, Lauffenburger DA, Nolan GP (2005) Protein-signaling networks derived from multiparameter single-cell data. Science 308:523-5299
    • (2005) Science , vol.308 , pp. 523-5299
    • Sachs, K.1    Perez, O.2    Peer, D.A.3    Lauffenburger, D.A.4    Nolan, G.P.5
  • 22
    • 84871893558 scopus 로고    scopus 로고
    • Bayes Net Toolbox for Matlab Cited 31 Dec 20100
    • Bayes Net Toolbox for Matlab. http://code. google.com/p/bnt/Cited 31 Dec 20100
  • 24
    • 15944361900 scopus 로고    scopus 로고
    • Informative structure priors: Joint learning of dynamic regulatory networks from multiple types of data
    • Bernard A, Hartemink A (2005) Informative structure priors: joint learning of dynamic regulatory networks from multiple types of data. Pacific symposium on biocomputing 2005 (PSB05), pp 459-4700
    • (2005) Pacific Symposium on Biocomputing 2005 (PSB05) , pp. 459-4700
    • Bernard, A.1    Hartemink, A.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.