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Volumn 17, Issue 1, 2016, Pages

TRaCE+: Ensemble inference of gene regulatory networks from transcriptional expression profiles of gene knock-out experiments

Author keywords

Design of experiments; Gene regulatory network; Network inference; Signed directed graph; Transitive reduction

Indexed keywords

CHEMICAL ACTIVATION; DESIGN OF EXPERIMENTS; DIRECTED GRAPHS; ESCHERICHIA COLI; GENES; GRAPH THEORY; INFERENCE ENGINES;

EID: 84975744575     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-016-1137-z     Document Type: Article
Times cited : (12)

References (20)
  • 1
    • 0014937223 scopus 로고
    • Central dogma of molecular biology
    • Crick F. Central dogma of molecular biology. Nature. 1970;227:561-3.
    • (1970) Nature , vol.227 , pp. 561-563
    • Crick, F.1
  • 2
    • 84928888210 scopus 로고    scopus 로고
    • Gene regulatory networks and their applications: understanding biological and medical problems in terms of networks
    • Emmert-Streib F, Dehmer M, Haibe-Kains B. Gene regulatory networks and their applications: understanding biological and medical problems in terms of networks. Front cell deve biol. 2014;2:38.
    • (2014) Front cell deve biol , vol.2 , pp. 38
    • Emmert-Streib, F.1    Dehmer, M.2    Haibe-Kains, B.3
  • 3
    • 14844286390 scopus 로고    scopus 로고
    • Reverse-engineering transcriptional control networks
    • Gardner TS, Faith JJ. Reverse-engineering transcriptional control networks. Phys Life Rev. 2005;2:65-88.
    • (2005) Phys Life Rev , vol.2 , pp. 65-88
    • Gardner, T.S.1    Faith, J.J.2
  • 4
    • 38449088751 scopus 로고    scopus 로고
    • Inferring cellular networks--a review
    • Markowetz F, Spang R. Inferring cellular networks--a review. BMC Bioinform. 2007;8 Suppl 6:S5.
    • (2007) BMC Bioinform , vol.8 , pp. S5
    • Markowetz, F.1    Spang, R.2
  • 6
    • 84860280305 scopus 로고    scopus 로고
    • Gene regulatory network inference: evaluation and application to ovarian cancer allows the prioritization of drug targets
    • Madhamshettiwar PB, Maetschke SR, Davis MJ, Reverter A, Ragan MA. Gene regulatory network inference: evaluation and application to ovarian cancer allows the prioritization of drug targets. Genome med. 2012;4:1-16.
    • (2012) Genome med , vol.4 , pp. 1-16
    • Madhamshettiwar, P.B.1    Maetschke, S.R.2    Davis, M.J.3    Reverter, A.4    Ragan, M.A.5
  • 7
    • 84892376552 scopus 로고    scopus 로고
    • Supervised, semi-supervised and unsupervised inference of gene regulatory networks
    • Maetschke SR, Madhamshettiwar PB, Davis MJ, Ragan MA. Supervised, semi-supervised and unsupervised inference of gene regulatory networks. Brief Bioinform. 2013;15:195-211.
    • (2013) Brief Bioinform , vol.15 , pp. 195-211
    • Maetschke, S.R.1    Madhamshettiwar, P.B.2    Davis, M.J.3    Ragan, M.A.4
  • 10
    • 84975680203 scopus 로고    scopus 로고
    • The DREAM Project
    • Consortium TD. The DREAM Project. 2006.
    • (2006) Consortium TD
  • 12
    • 84905640975 scopus 로고    scopus 로고
    • Ensemble Inference and Inferability of Gene Regulatory Networks
    • Ud-Dean SMM, Gunawan R. Ensemble Inference and Inferability of Gene Regulatory Networks. PLoS One. 2014;9:e103812.
    • (2014) PLoS One , vol.9 , pp. e103812
    • Ud-Dean, S.M.M.1    Gunawan, R.2
  • 13
    • 77955867869 scopus 로고    scopus 로고
    • TRANSWESD: inferring cellular networks with transitive reduction
    • Klamt S, Flassig RJ, Sundmacher K. TRANSWESD: inferring cellular networks with transitive reduction. Bioinformatics. 2010;26:2160-8.
    • (2010) Bioinformatics , vol.26 , pp. 2160-2168
    • Klamt, S.1    Flassig, R.J.2    Sundmacher, K.3
  • 14
    • 84881178444 scopus 로고    scopus 로고
    • Reconstruction of large-scale regulatory networks based on perturbation graphs and transitive reduction: improved methods and their evaluation
    • Pinna A, Heise S, Flassig RJ, de la Fuente A, Klamt S. Reconstruction of large-scale regulatory networks based on perturbation graphs and transitive reduction: improved methods and their evaluation. BMC Syst Biol. 2013;7:73.
    • (2013) BMC Syst Biol , vol.7 , pp. 73
    • Pinna, A.1    Heise, S.2    Flassig, R.J.3    Fuente, A.4    Klamt, S.5
  • 15
    • 84962173551 scopus 로고    scopus 로고
    • Optimal design of gene knock-out experiments for gene regulatory network inference
    • Ud-Dean SM, Gunawan R. Optimal design of gene knock-out experiments for gene regulatory network inference. Bioinformatics. 2015;32:875-883.
    • (2015) Bioinformatics , vol.32 , pp. 875-883
    • Ud-Dean, S.M.1    Gunawan, R.2
  • 17
    • 79961200389 scopus 로고    scopus 로고
    • GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods
    • Schaffter T, Marbach D, Floreano D. GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods. Bioinformatics. 2011;27:2263-70.
    • (2011) Bioinformatics , vol.27 , pp. 2263-2270
    • Schaffter, T.1    Marbach, D.2    Floreano, D.3
  • 19
    • 0001422329 scopus 로고
    • Quantitative model for gene regulation by lambda phage repressor
    • Ackers GK, Johnson AD, Shea MA. Quantitative model for gene regulation by lambda phage repressor. Proc Natl Acad Sci. 1982;79:1129-33.
    • (1982) Proc Natl Acad Sci , vol.79 , pp. 1129-1133
    • Ackers, G.K.1    Johnson, A.D.2    Shea, M.A.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.