메뉴 건너뛰기




Volumn 27, Issue 13, 2011, Pages 1832-1838

A multiple network learning approach to capture system-wide condition-specific responses

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; ARTIFICIAL INTELLIGENCE; BIOLOGY; COMPUTER SIMULATION; CYTOLOGY; GENETICS; METHODOLOGY; PHYSIOLOGY; SACCHAROMYCES CEREVISIAE;

EID: 79959479526     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btr270     Document Type: Article
Times cited : (9)

References (33)
  • 1
    • 41649110034 scopus 로고    scopus 로고
    • Characterization of differentiated quiescent and non-quiescent cells in yeast stationary-phase cultures
    • Aragon, A.D. et al. (2008) Characterization of differentiated quiescent and non-quiescent cells in yeast stationary-phase cultures. Mol. Biol. Cell, 19, 1271-1280.
    • (2008) Mol. Biol. Cell , vol.19 , pp. 1271-1280
    • Aragon, A.D.1
  • 2
    • 19344374050 scopus 로고    scopus 로고
    • Similarities and differences in genome-wide expression data of six organisms
    • Bergmann, S. et al. (2004) Similarities and differences in genome-wide expression data of six organisms. PLoS Biol., 2, E9.
    • (2004) PLoS Biol. , vol.2
    • Bergmann, S.1
  • 3
    • 0017751656 scopus 로고
    • Efficiency of pseudolikelihood estimation for simple gaussian fields
    • Besag,J. (1977) Efficiency of pseudolikelihood estimation for simple gaussian fields. Biometrika, 64, 616-618.
    • (1977) Biometrika , vol.64 , pp. 616-618
    • Besag, J.1
  • 4
    • 59149103774 scopus 로고    scopus 로고
    • Coordinated concentration changes of transcripts and metabolites in Saccharomyces cerevisiae
    • Bradley, P.H. et al. (2009) Coordinated concentration changes of transcripts and metabolites in Saccharomyces cerevisiae. PLoS Comput. Biol., 5, e1000270.
    • (2009) PLoS Comput. Biol. , vol.5
    • Bradley, P.H.1
  • 5
    • 35348891430 scopus 로고    scopus 로고
    • Network-based classification of breast cancer metastasis
    • Chuang, H.-Y. et al. (2007) Network-based classification of breast cancer metastasis. Mol. Syst. Biol., 3.
    • (2007) Mol. Syst. Biol. , pp. 3
    • Chuang, H.-Y.1
  • 6
    • 79953317636 scopus 로고    scopus 로고
    • The proteomics of quiescent and non-quiescent cell differentiation in yeast stationary-phase cultures
    • Davidson, G.S. et al. (2011) The proteomics of quiescent and non-quiescent cell differentiation in yeast stationary-phase cultures. Mol. Biol. Cell, 22, 988-998.
    • (2011) Mol. Biol. Cell , vol.22 , pp. 988-998
    • Davidson, G.S.1
  • 7
    • 0033707946 scopus 로고    scopus 로고
    • Using bayesian networks to analyze expression data
    • Friedman, N. 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
  • 8
    • 2942584864 scopus 로고    scopus 로고
    • 'sleeping beauty': Quiescence in Saccharomyces cerevisiae
    • Gray, J.V. et al. (2004) 'sleeping beauty': Quiescence in Saccharomyces cerevisiae. Microbiol. Mol. Biol. Rev., 68, 187-206.
    • (2004) Microbiol. Mol. Biol. Rev. , vol.68 , pp. 187-206
    • Gray, J.V.1
  • 9
    • 4544352942 scopus 로고    scopus 로고
    • Transcriptional regulatory code of a eukaryotic genome
    • Harbison, C.T. et al. (2004) Transcriptional regulatory code of a eukaryotic genome. Nature, 431, 99-104.
    • (2004) Nature , vol.431 , pp. 99-104
    • Harbison, C.T.1
  • 11
    • 0002370418 scopus 로고    scopus 로고
    • A Tutorial on Learning with Bayesian Networks
    • Jordan,M. (ed.) MIT Press, Cambridge, MA
    • Heckerman,D. (1999) A Tutorial on Learning with Bayesian Networks. In Jordan,M. (ed.) Learning in Graphical Models, MIT Press, Cambridge, MA.
    • (1999) Learning in Graphical Models
    • Heckerman, D.1
  • 12
    • 62649085538 scopus 로고    scopus 로고
    • The DNA-encoded nucleosome organization of a eukaryotic genome
    • Kaplan, N. et al. (2008) The DNA-encoded nucleosome organization of a eukaryotic genome. Nature, 458, 362-366.
    • (2008) Nature , vol.458 , pp. 362-366
    • Kaplan, N.1
  • 13
    • 33745797057 scopus 로고    scopus 로고
    • Unraveling condition specific gene transcriptional regulatory networks in saccharomyces cerevisiae
    • Kim, H. et al. (2006) Unraveling condition specific gene transcriptional regulatory networks in saccharomyces cerevisiae. BMC Bioinformatics.
    • (2006) BMC Bioinformatics
    • Kim, H.1
  • 14
    • 0004047518 scopus 로고    scopus 로고
    • Oxford Statistical Science Series. Oxford University Press, New York, USA
    • Lauritzen, S.L. (1996) Graphical Models. Oxford Statistical Science Series. Oxford University Press, New York, USA.
    • (1996) Graphical Models
    • Lauritzen, S.L.1
  • 15
    • 9444282110 scopus 로고    scopus 로고
    • Genomic analysis of stationary-phase and exit in Saccharomyces cerevisiae: gene expression and identification of novel essential genes
    • Martinez,M.J. et al. (2004) Genomic analysis of stationary-phase and exit in Saccharomyces cerevisiae: gene expression and identification of novel essential genes. Mol. Biol. Cell, 15, 5295-5305.
    • (2004) Mol. Biol. Cell , vol.15 , pp. 5295-5305
    • Martinez, M.J.1
  • 16
    • 2942694772 scopus 로고    scopus 로고
    • Artificial gene networks for objective comparison of analysis algorithms
    • Mendes, P. et al. (2003) Artificial gene networks for objective comparison of analysis algorithms. Bioinformatics, 19, 122-129.
    • (2003) Bioinformatics , vol.19 , pp. 122-129
    • Mendes, P.1
  • 17
    • 34548749776 scopus 로고    scopus 로고
    • Context-sensitive data integration and prediction of biological networks
    • Myers, C.L. and Troyanskaya,O.G. (2007) Context-sensitive data integration and prediction of biological networks. Bioinformatics, 23, 2322-2330.
    • (2007) Bioinformatics , vol.23 , pp. 2322-2330
    • Myers, C.L.1    Troyanskaya, O.G.2
  • 18
    • 18144442687 scopus 로고    scopus 로고
    • Inferring subnetworks from perturbed expression profiles
    • Pe'er, D. et al. (2001) Inferring subnetworks from perturbed expression profiles. Bioinformatics, 17 (Suppl. 1), S215-S224.
    • (2001) Bioinformatics , vol.17 , Issue.SUPPL. 1
    • Pe'er, D.1
  • 19
    • 33646338193 scopus 로고    scopus 로고
    • Minreg: a scalable algorithm for learning parsimonious regulatory networks in yeast and mammals
    • Pe'er,D. et al. (2006) Minreg: a scalable algorithm for learning parsimonious regulatory networks in yeast and mammals. J. Mach. Learn. Res., 7, 167-189.
    • (2006) J. Mach. Learn. Res. , vol.7 , pp. 167-189
    • Pe'er, D.1
  • 20
    • 34848903220 scopus 로고    scopus 로고
    • From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data
    • Rhein,R.O. and Strimmer,K. (2007) From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data. BMC Syst. Biol., 1, 37.
    • (2007) BMC Syst. Biol. , vol.1 , pp. 37
    • Rhein, R.O.1    Strimmer, K.2
  • 21
    • 33846675685 scopus 로고    scopus 로고
    • Similarities and differences of gene expression in yeast stress conditions
    • Rokhlenko, O. et al. (2007) Similarities and differences of gene expression in yeast stress conditions. Bioinformatics, 23, e184-e190.
    • (2007) Bioinformatics , vol.23
    • Rokhlenko, O.1
  • 22
    • 61949323210 scopus 로고    scopus 로고
    • Inference of functional networks of condition-specific response- a case study of quiescence in yeast
    • Roy, S. et al. (2009) Inference of functional networks of condition-specific response- a case study of quiescence in yeast. In Proceedings of Pacific Symposium on Biocomputing. pp. 51-62.
    • (2009) Proceedings of Pacific Symposium on Biocomputing , pp. 51-62
    • Roy, S.1
  • 23
    • 79959405068 scopus 로고    scopus 로고
    • Correct citation of Roy et al. 2009 is: Roy et al
    • Correct citation of Roy et al. 2009 is: Roy et al. (2009).
    • (2009)
  • 24
    • 33644873438 scopus 로고    scopus 로고
    • Regulondb (version 5.0): Escherichia coli k-12 transcriptional regulatory network, operon organization, and growth conditions
    • Salgado,H. et al. (2006) Regulondb (version 5.0): Escherichia coli k-12 transcriptional regulatory network, operon organization, and growth conditions. Nucleic Acids Res., 34, D394.
    • (2006) Nucleic Acids Res. , vol.34
    • Salgado, H.1
  • 25
    • 41949107946 scopus 로고    scopus 로고
    • Mmg: a probabilistic tool to identify submodules of metabolic pathways
    • Sanguinetti, G. et al. (2008) Mmg: a probabilistic tool to identify submodules of metabolic pathways. Bioinformatics, 24, 1078-1084.
    • (2008) Bioinformatics , vol.24 , pp. 1078-1084
    • Sanguinetti, G.1
  • 26
    • 15944364151 scopus 로고    scopus 로고
    • An empirical bayes approach to inferring large-scale gene association networks
    • Schäfer,J. and Strimmer,K. (2005) An empirical bayes approach to inferring large-scale gene association networks. Bioinformatics, 21, 754-764.
    • (2005) Bioinformatics , vol.21 , pp. 754-764
    • Schäfer, J.1    Strimmer, K.2
  • 27
    • 0037941585 scopus 로고    scopus 로고
    • Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data
    • Segal, E. et al. (2003) Module networks: identifying 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
  • 28
    • 21844455527 scopus 로고    scopus 로고
    • Learning module networks
    • Segal, E. et al. (2005) Learning module networks. J. Mach. Learn. Res., 6, 557-588.
    • (2005) J. Mach. Learn. Res. , vol.6 , pp. 557-588
    • Segal, E.1
  • 29
    • 0141993704 scopus 로고    scopus 로고
    • Agene-coexpression network for global discovery of conserved genetic modules
    • Stuart, J.M. et al. (2003)Agene-coexpression network for global discovery of conserved genetic modules. Science, 302, 249-255.
    • (2003) Science , vol.302 , pp. 249-255
    • Stuart, J.M.1
  • 30
    • 33745656175 scopus 로고    scopus 로고
    • Characterizing disease states from topological properties of transcriptional regulatory networks
    • Tuck, D.P. et al. (2006) Characterizing disease states from topological properties of transcriptional regulatory networks. BMC Bioinformatics, 7.
    • (2006) BMC Bioinformatics , pp. 7
    • Tuck, D.P.1
  • 31
    • 33749825955 scopus 로고    scopus 로고
    • Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and Bayesian networks
    • Werhli, A.V. et al. (2006) Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and Bayesian networks. Bioinformatics, 22, 2523-2531.
    • (2006) Bioinformatics , vol.22 , pp. 2523-2531
    • Werhli, A.V.1
  • 32
    • 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
  • 33
    • 79959464522 scopus 로고    scopus 로고
    • Differential dependency network analysis to identify conditionspecific topological changes in biological networks
    • Zhang, B. et al. (2008) Differential dependency network analysis to identify conditionspecific topological changes in biological networks. Bioinformatics. 1838.
    • (2008) Bioinformatics , pp. 1838
    • Zhang, B.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.