메뉴 건너뛰기




Volumn 6, Issue , 2005, Pages

Learning module networks

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA ACQUISITION; GENES; GENETIC ENGINEERING; NEURAL NETWORKS; STATISTICAL METHODS;

EID: 21844455527     PISSN: 15337928     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (108)

References (32)
  • 7
    • 34249832377 scopus 로고
    • A Bayesian method for the induction of probabilistic networks from data
    • G. F. Cooper and E. Herskovits. A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9:309-347, 1992.
    • (1992) Machine Learning , vol.9 , pp. 309-347
    • Cooper, G.F.1    Herskovits, E.2
  • 8
    • 84990553353 scopus 로고
    • A model for reasoning about persistence and causation
    • T. Dean and K. Kanazawa. A model for reasoning about persistence and causation. Computational Intelligence, 5:142-150, 1989.
    • (1989) Computational Intelligence , vol.5 , pp. 142-150
    • Dean, T.1    Kanazawa, K.2
  • 13
    • 0033637153 scopus 로고    scopus 로고
    • Genomic expression program in the response of yeast cells to environmental changes
    • A. P. Gasch et al. Genomic expression program in the response of yeast cells to environmental changes. Mol. Bio. Cell, 11:4241-4257, 2000.
    • (2000) Mol. Bio. Cell , vol.11 , pp. 4241-4257
    • Gasch, A.P.1
  • 14
    • 0000220520 scopus 로고    scopus 로고
    • Learning Bayesian networks with local structure
    • M. I. Jordan, editor, Kluwer, Dordrecht, Netherlands
    • N. Friedman and M. Goldszmidt. Learning Bayesian networks with local structure. In M. I. Jordan, editor, Learning in Graphical Models, pages 421-460. Kluwer, Dordrecht, Netherlands, 1998.
    • (1998) Learning in Graphical Models , pp. 421-460
    • Friedman, N.1    Goldszmidt, M.2
  • 15
    • 0037262841 scopus 로고    scopus 로고
    • Being Bayesian about Bayesian network structure: A Bayesian approach to structure discovery in Bayesian networks
    • N. Friedman and D. Koller. Being Bayesian about Bayesian network structure: A Bayesian approach to structure discovery in Bayesian networks. Machine Learning, 50:95-126, 2003.
    • (2003) Machine Learning , vol.50 , pp. 95-126
    • Friedman, N.1    Koller, D.2
  • 17
    • 0002219642 scopus 로고    scopus 로고
    • Data analysis with Bayesian networks: A bootstrap approach
    • N. Friedman, M. Goldszmidt, and A. Wyner. Data analysis with Bayesian networks: A bootstrap approach. In Proc. UAI, pages 206-215, 1999.
    • (1999) Proc. UAI , pp. 206-215
    • Friedman, N.1    Goldszmidt, M.2    Wyner, A.3
  • 21
    • 34249761849 scopus 로고
    • Learning Bayesian networks: The combination of knowledge and statistical data
    • D. Heckerman, D. Geiger, and D. M. Chickering. Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20:197-243, 1995.
    • (1995) Machine Learning , vol.20 , pp. 197-243
    • Heckerman, D.1    Geiger, D.2    Chickering, D.M.3
  • 22
    • 0002370418 scopus 로고    scopus 로고
    • A tutorial on learning with Bayesian networks
    • M. I. Jordan, editor, Kluwer, Dordrecht, Netherlands
    • D. Heckerman. A tutorial on learning with Bayesian networks. In M. I. Jordan, editor, Learning in Graphical Models. Kluwer, Dordrecht, Netherlands, 1998.
    • (1998) Learning in Graphical Models
    • Heckerman, D.1
  • 26
    • 84984932880 scopus 로고    scopus 로고
    • Array of hope
    • E. Lander. Array of hope. Nature Genetics, 21:3-4, 1999.
    • (1999) Nature Genetics , vol.21 , pp. 3-4
    • Lander, E.1
  • 27
    • 2342533144 scopus 로고    scopus 로고
    • Fusion of domain knowledge with data for structural learning in object oriented domains
    • H. Langseth and T. D. Nielsen. Fusion of domain knowledge with data for structural learning in object oriented domains. Machine Learning Research, 4:339-368, 2003.
    • (2003) Machine Learning Research , vol.4 , pp. 339-368
    • Langseth, H.1    Nielsen, T.D.2
  • 28
    • 18144442687 scopus 로고    scopus 로고
    • Inferring subnetworks from perturbed expression profiles
    • D. Pe'er, A. Regev, G. Elidan, and N. Friedman. Inferring subnetworks from perturbed expression profiles. Bioinformatics, 17(Suppl 1):S215-24, 2001.
    • (2001) Bioinformatics , vol.17 , Issue.SUPPL. 1 , pp. 215-224
    • Pe'er, D.1    Regev, A.2    Elidan, G.3    Friedman, N.4
  • 29
    • 0346532088 scopus 로고
    • The Bonferroni and the Scheffe multiple comparison procedures
    • N. E. Savin. The Bonferroni and the Scheffe multiple comparison procedures. Review of Economic Studies, 47(l):255-73, 1980.
    • (1980) Review of Economic Studies , vol.47 , Issue.50 , pp. 255-273
    • Savin, N.E.1
  • 30
    • 0035237805 scopus 로고    scopus 로고
    • Rich probabilistic models for gene expression
    • E. Segal, B. Taskar, A. Gasch, N. Friedman, and D. Koller. Rich probabilistic models for gene expression. Bioinformatics, 17(Suppl 1):S243-52, 2001.
    • (2001) Bioinformatics , vol.17 , Issue.SUPPL. 1 , pp. 243-252
    • Segal, E.1    Taskar, B.2    Gasch, A.3    Friedman, N.4    Koller, D.5
  • 32
    • 0037941585 scopus 로고    scopus 로고
    • Module networks: Discovering regulatory modules and their condition specific regulators from gene expression data
    • E. Segal, M. Shapira, A. Regev, D. Pe'er, D. Botstein, D. Koller, and N. Friedman. Module networks: Discovering regulatory modules and their condition specific regulators from gene expression data. Nature Genetics, 34(2): 166-176, 2003.
    • (2003) Nature Genetics , vol.34 , Issue.2 , pp. 166-176
    • Segal, E.1    Shapira, M.2    Regev, A.3    Pe'er, D.4    Botstein, D.5    Koller, D.6    Friedman, N.7


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.