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Volumn 1115, Issue , 2007, Pages 240-248

A framework for elucidating regulatory networks based on prior information and expression data

Author keywords

Bayesian networks; Prior information; Regulatory networks

Indexed keywords

BAYES THEOREM; CONFERENCE PAPER; ENGINEERING; INFORMATION; MEDLINE; METHODOLOGY; MODEL; PROTEIN DNA INTERACTION; REGULATORY NETWORK; TAXONOMY;

EID: 36248991815     PISSN: 00778923     EISSN: 17496632     Source Type: Book Series    
DOI: 10.1196/annals.1407.002     Document Type: Conference Paper
Times cited : (18)

References (17)
  • 1
    • 0033707946 scopus 로고    scopus 로고
    • Using Bayesian networks to analyze expression data
    • FRIEDMAN, N., M. LINIAL, I. NACHMAN, et al. 2000. Using Bayesian networks to analyze expression data. J. Comput. Biol. 7: 601-620.
    • (2000) J. Comput. Biol , vol.7 , pp. 601-620
    • FRIEDMAN, N.1    LINIAL, M.2    NACHMAN, I.3
  • 2
    • 0037941585 scopus 로고    scopus 로고
    • Module networks: Indentifying regulatory modules and their condition-specific regulators from gene expression data
    • SEGAL, E., M. SHAPIRA, A. REGEV, et al. 2003. Module networks: indentifying regulatory modules and their condition-specific regulators from gene expression data. Nat. Genet. 34: 166-176.
    • (2003) Nat. Genet , vol.34 , pp. 166-176
    • SEGAL, E.1    SHAPIRA, M.2    REGEV, A.3
  • 3
    • 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
  • 4
    • 3042698613 scopus 로고    scopus 로고
    • Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network
    • IMOTO, S., S. KIM, T. GOTO, et al. 2003. Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network. J. Bioinform. Comput. Biol. 1: 231-252.
    • (2003) J. Bioinform. Comput. Biol , vol.1 , pp. 231-252
    • IMOTO, S.1    KIM, S.2    GOTO, T.3
  • 5
    • 33745812835 scopus 로고    scopus 로고
    • Predicting the outcome of pregnancies of unknown location: Bayesian networks with expert prior information compared to logistic regression
    • GEVAERT, O., F. DE SMET, E. KIRK, et al. 2006. Predicting the outcome of pregnancies of unknown location: Bayesian networks with expert prior information compared to logistic regression. Hum. Reprod. 21 (7): 1824-1831.
    • (2006) Hum. Reprod , vol.21 , Issue.7 , pp. 1824-1831
    • GEVAERT, O.1    DE SMET, F.2    KIRK, E.3
  • 6
    • 1842714948 scopus 로고    scopus 로고
    • Using literature and data to learn Bayesian networks as clinical models of ovarian tumours
    • ANTAL, P., G. FANNES, D. TIMMERMAN, et al. 2004. Using literature and data to learn Bayesian networks as clinical models of ovarian tumours. Artif. Intel. Med. 30: 257-281.
    • (2004) Artif. Intel. Med , vol.30 , pp. 257-281
    • ANTAL, P.1    FANNES, G.2    TIMMERMAN, D.3
  • 9
    • 34249832377 scopus 로고
    • A Bayesian method for the induction of probabilistic networks from data
    • COOPER, G.F. & E. HERSKOVITS. 1992. A Bayesian method for the induction of probabilistic networks from data. Machine Learning 9 (4): 309-347.
    • (1992) Machine Learning , vol.9 , Issue.4 , pp. 309-347
    • COOPER, G.F.1    HERSKOVITS, E.2
  • 10
    • 33747891871 scopus 로고    scopus 로고
    • Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks
    • GEVAERT, O., F. DE SMET, D. TIMMERMAN, et al. 2006. Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks. Bioinformatics 22: e184-e190.
    • (2006) Bioinformatics , vol.22
    • GEVAERT, O.1    DE SMET, F.2    TIMMERMAN, D.3
  • 11
    • 34249761849 scopus 로고
    • Learning Bayesian networks: The combination of knowledge and statistical data
    • HECKERMAN, D., D. GEIGER & D.M. CHICKERING. 1995. Learning Bayesian networks: the combination of knowledge and statistical data. Machine Learning 20: 197-243.
    • (1995) Machine Learning , vol.20 , pp. 197-243
    • HECKERMAN, D.1    GEIGER, D.2    CHICKERING, D.M.3
  • 12
    • 13444272070 scopus 로고    scopus 로고
    • ALFARANO, C., C.E. ANDRADE, K. ANTHONY, et al. 2005. The Biomolecular Interaction Network Database and related tools 2005 update. Nucleic Acids Res. 1; 33 (Database issue): D418-D424.
    • ALFARANO, C., C.E. ANDRADE, K. ANTHONY, et al. 2005. The Biomolecular Interaction Network Database and related tools 2005 update. Nucleic Acids Res. 1; 33 (Database issue): D418-D424.
  • 13
    • 33644876958 scopus 로고    scopus 로고
    • MATYS, V., O.V. KEL-MARGOULIS, E. FRICKE, et al. 2006. TRANSFAC(R) and its module TRANSCompel(R): transcriptional gene regulation in eukaryotes. Nucleic Acids Res. 1; 34 (Database issue): D108-D110.
    • MATYS, V., O.V. KEL-MARGOULIS, E. FRICKE, et al. 2006. TRANSFAC(R) and its module TRANSCompel(R): transcriptional gene regulation in eukaryotes. Nucleic Acids Res. 1; 34 (Database issue): D108-D110.
  • 14
    • 9144248312 scopus 로고    scopus 로고
    • HERMJAKOB, H., L. MONTECCHI-PALAZZI, C. LEWINGTON, et al. 2004. IntAct: an open source molecular interaction database. Nucl. Acids Res. 1; 32 (Database issue): D452-D455.
    • HERMJAKOB, H., L. MONTECCHI-PALAZZI, C. LEWINGTON, et al. 2004. IntAct: an open source molecular interaction database. Nucl. Acids Res. 1; 32 (Database issue): D452-D455.
  • 15
    • 31144459985 scopus 로고    scopus 로고
    • BILD, A., G. YAO, J. CHANG, et al. 2005. Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 19; 439 (7074): 353-357.
    • BILD, A., G. YAO, J. CHANG, et al. 2005. Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 19; 439 (7074): 353-357.
  • 16
    • 0242490780 scopus 로고    scopus 로고
    • Cytoscape: A software environment for integrated models of biomolecular interaction networks
    • SHANNON, P., A. MARKIEL, O. OZIER, et al. 2003. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13: 2498-2504.
    • (2003) Genome Res , vol.13 , pp. 2498-2504
    • SHANNON, P.1    MARKIEL, A.2    OZIER, O.3
  • 17
    • 18244409687 scopus 로고    scopus 로고
    • Gene expression profiling predicts clinical outcome of breast cancer
    • VAN'T VEER, L.J., H. DAI, M.J. VAN DE VIJVER, et al. 2002. Gene expression profiling predicts clinical outcome of breast cancer. Nature 415: 530-536.
    • (2002) Nature , vol.415 , pp. 530-536
    • VAN'T VEER, L.J.1    DAI, H.2    VAN DE VIJVER, M.J.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.