메뉴 건너뛰기




Volumn 30, Issue 17, 2014, Pages

Causal network inference using biochemical kinetics

Author keywords

[No Author keywords available]

Indexed keywords

BAYES THEOREM; CHEMICAL MODEL; HUMAN; KINETICS; SIGNAL TRANSDUCTION; TUMOR CELL LINE;

EID: 84907019497     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btu452     Document Type: Conference Paper
Times cited : (56)

References (35)
  • 1
    • 70449375094 scopus 로고    scopus 로고
    • Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics
    • Aijö , T. and Lähdesmäki, H. (2010) Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics. Bioinformatics, 25, 2937-2944.
    • (2010) Bioinformatics , vol.25 , pp. 2937-2944
    • Aijö, T.1    Lähdesmäki, H.2
  • 2
    • 33645307955 scopus 로고    scopus 로고
    • Inference of gene regulatory networks and compound mode of action from time course gene expression profiles
    • Bansal, M. et al. (2006) Inference of gene regulatory networks and compound mode of action from time course gene expression profiles. Bioinformatics, 22, 815-822.
    • (2006) Bioinformatics , vol.22 , pp. 815-822
    • Bansal, M.1
  • 3
    • 84859758552 scopus 로고    scopus 로고
    • Statistical analysis of nonlinear dynamical systems using differential geometric sampling methods
    • Calderhead, B. and Girolami, M. (2011) Statistical analysis of nonlinear dynamical systems using differential geometric sampling methods. J. R. Soc. Interface Focus, 1, 821-835.
    • (2011) J. R. Soc. Interface Focus , vol.1 , pp. 821-835
    • Calderhead, B.1    Girolami, M.2
  • 4
    • 58549083142 scopus 로고    scopus 로고
    • Input-output behavior of ErbB signaling pathways as revealed by a mass action model trained against dynamic data
    • Chen, W.W. et al. (2009) Input-output behavior of ErbB signaling pathways as revealed by a mass action model trained against dynamic data. Mol. Syst. Biol., 5, 239.
    • (2009) Mol. Syst. Biol , vol.5 , pp. 239
    • Chen, W.W.1
  • 5
    • 1842715143 scopus 로고    scopus 로고
    • Marginal likelihood from the metropolis-hastings output
    • Chib, S. and Jeliazkov, I. (2001) Marginal likelihood from the metropolis-hastings output. J. Am. Stat. Assoc., 96, 270-281.
    • (2001) J. Am. Stat. Assoc , vol.96 , pp. 270-281
    • Chib, S.1    Jeliazkov, I.2
  • 6
    • 84880569964 scopus 로고    scopus 로고
    • Non-homogeneous dynamic Bayesian networks with Bayesian regularization for inferring gene regulatory networks with gradually time-varying structure
    • Dondelinger, F. et al. (2012) Non-homogeneous dynamic Bayesian networks with Bayesian regularization for inferring gene regulatory networks with gradually time-varying structure. Mach. Learn., 90, 191-230.
    • (2012) Mach. Learn , vol.90 , pp. 191-230
    • Dondelinger, F.1
  • 9
    • 49549100459 scopus 로고    scopus 로고
    • Learning causal bayesian network structures from experimental data
    • Ellis, B. and Wong, W. (2008) Learning causal bayesian network structures from experimental data. J. Am. Stat. Assoc., 103, 778-789.
    • (2008) J. Am. Stat. Assoc , vol.103 , pp. 778-789
    • Ellis, B.1    Wong, W.2
  • 10
    • 0038048325 scopus 로고    scopus 로고
    • Inferring genetic networks and identifying compound mode of action via expression profiling
    • Gardner, T. et al. (2003) Inferring genetic networks and identifying compound mode of action via expression profiling. Science, 301, 102-105.
    • (2003) Science , vol.301 , pp. 102-105
    • Gardner, T.1
  • 11
    • 84879237918 scopus 로고    scopus 로고
    • Inferring latent gene regulatory network kinetics
    • González, J. et al. (2013) Inferring latent gene regulatory network kinetics. Stat. Appl. Genet. Mol., 12, 109-127.
    • (2013) Stat. Appl. Genet. Mol , vol.12 , pp. 109-127
    • González, J.1
  • 12
    • 78649694411 scopus 로고    scopus 로고
    • A technical assessment of the utility of reverse phase protein arrays for the study of the functional proteome in nonmicrodissected human breast cancer
    • Hennessy, B.T. et al. (2010) A technical assessment of the utility of reverse phase protein arrays for the study of the functional proteome in nonmicrodissected human breast cancer. Clin. Proteomics, 6, 129-151.
    • (2010) Clin. Proteomics , vol.6 , pp. 129-151
    • Hennessy, B.T.1
  • 13
    • 84868020742 scopus 로고    scopus 로고
    • Bayesian inference of signaling network topology in a cancer cell line
    • Hill, S.M. et al. (2012) Bayesian inference of signaling network topology in a cancer cell line. Bioinformatics, 28, 2804-2810.
    • (2012) Bioinformatics , vol.28 , pp. 2804-2810
    • Hill, S.M.1
  • 14
    • 77952328474 scopus 로고    scopus 로고
    • Model-based method for transcription factor target identification with limited data
    • Honkela, A. et al. (2010) Model-based method for transcription factor target identification with limited data. Proc. Natl Acad. Sci. USA, 107, 7793-7798.
    • (2010) Proc. Natl Acad. Sci. USA , vol.107 , pp. 7793-7798
    • Honkela, A.1
  • 15
    • 27944462549 scopus 로고
    • A reference Bayesian test for nested hypotheses and its relationship to the Schwarz criterion
    • Kass, R.E. and Wasserman, L. (1995) A reference Bayesian test for nested hypotheses and its relationship to the Schwarz criterion. J. Am. Stat. Assoc., 90, 928-934.
    • (1995) J. Am. Stat. Assoc , vol.90 , pp. 928-934
    • Kass, R.E.1    Wasserman, L.2
  • 16
    • 33644524741 scopus 로고    scopus 로고
    • Cell-signalling dynamics in time and space
    • Kholodenko, B. (2006) Cell-signalling dynamics in time and space. Nat. Rev. Mol. Cell Biol., 7, 165-176.
    • (2006) Nat. Rev. Mol. Cell Biol , vol.7 , pp. 165-176
    • Kholodenko, B.1
  • 18
    • 69949166983 scopus 로고    scopus 로고
    • Estimating high-dimensional intervention effects from observational data
    • Maathuis, M.H. et al. (2009) Estimating high-dimensional intervention effects from observational data. Ann. Stat., 37, 3133-3164.
    • (2009) Ann. Stat , vol.37 , pp. 3133-3164
    • Maathuis, M.H.1
  • 19
    • 55749093996 scopus 로고    scopus 로고
    • Network inference using informative priors
    • Mukherjee, S. and Speed, T. (2008) Network inference using informative priors. Proc. Natl Acad. Sci. USA, 105, 14313-14318.
    • (2008) Proc. Natl Acad. Sci. USA , vol.105 , pp. 14313-14318
    • Mukherjee, S.1    Speed, T.2
  • 20
    • 14844307159 scopus 로고    scopus 로고
    • Inferring quantitative models of regulatory networks from expression data
    • Nachman, I. et al. (2004) Inferring quantitative models of regulatory networks from expression data. Bioinformatics, 20, i248-i256.
    • (2004) Bioinformatics , vol.20
    • Nachman, I.1
  • 21
    • 51049117937 scopus 로고    scopus 로고
    • Models from experiments: Combinatorial drug perturbations of cancer cells
    • Nelander, S. et al. (2008) Models from experiments: combinatorial drug perturbations of cancer cells. Mol. Syst. Biol., 4, 216.
    • (2008) Mol. Syst. Biol , vol.4 , pp. 216
    • Nelander, S.1
  • 22
    • 33845209913 scopus 로고    scopus 로고
    • A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes
    • Neve, R. et al. (2006) A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes. Cancer Cell, 10, 515-527.
    • (2006) Cancer Cell , vol.10 , pp. 515-527
    • Neve, R.1
  • 23
    • 84866463900 scopus 로고    scopus 로고
    • Network inference and biological dynamics
    • Oates, C.J. and Mukherjee, S. (2012) Network inference and biological dynamics. Ann. Appl. Stat., 6, 1209-1235.
    • (2012) Ann. Appl. Stat , vol.6 , pp. 1209-1235
    • Oates, C.J.1    Mukherjee, S.2
  • 24
    • 84866453034 scopus 로고    scopus 로고
    • Network inference using steady state data and Goldbeter- Koshland kinetics
    • Oates, C.J. et al. (2012) Network inference using steady state data and Goldbeter- Koshland kinetics. Bioinformatics, 28, 2342-2348.
    • (2012) Bioinformatics , vol.28 , pp. 2342-2348
    • Oates, C.J.1
  • 25
    • 77649325496 scopus 로고    scopus 로고
    • Causal inference in statistics: An overview
    • Pearl, J. (2009) Causal inference in statistics: an overview. Stat. Surv., 3, 96-146.
    • (2009) Stat. Surv , vol.3 , pp. 96-146
    • Pearl, J.1
  • 26
    • 80053168312 scopus 로고    scopus 로고
    • Identifiability of causal graphs using functional models
    • Barcelona, Spain
    • Peters, J. et al. (2011) Identifiability of causal graphs using functional models. In: 27th Conference on Uncertainty in Artificial Intelligence, Barcelona, Spain. pp. 589-598.
    • (2011) 27th Conference on Uncertainty in Artificial Intelligence , pp. 589-598
    • Peters, J.1
  • 27
    • 35648981518 scopus 로고    scopus 로고
    • Parameter estimation for differential equations: A generalized smoothing approach
    • Ramsay, J.O. (2007) Parameter estimation for differential equations: a generalized smoothing approach. J. R. Stat. Soc. Series B Stat. Methodol., 69, 741-796.
    • (2007) J. R. Stat. Soc. Series B Stat. Methodol , vol.69 , pp. 741-796
    • Ramsay, J.O.1
  • 28
    • 62849120031 scopus 로고    scopus 로고
    • Approximate Bayesian inference for latent Gaussian models using integrated nested laplace approximations
    • Rue, H. et al. (2009) Approximate Bayesian inference for latent Gaussian models using integrated nested laplace approximations. J. R. Stat. Soc. Series B Stat. Methodol., 71, 319-392.
    • (2009) J. R. Stat. Soc. Series B Stat. Methodol , vol.71 , pp. 319-392
    • Rue, H.1
  • 29
    • 17644427718 scopus 로고    scopus 로고
    • Causal Protein-signaling networks derived from multiparameter single-cell data
    • Sachs, K. et al. (2005) Causal Protein-signaling networks derived from multiparameter single-cell data. Science, 308, 523-529.
    • (2005) Science , vol.308 , pp. 523-529
    • Sachs, K.1
  • 30
    • 77953071298 scopus 로고    scopus 로고
    • Bayes and empirical-bayes multiplicity adjustment in the variable-selection problem
    • Scott, J.G. and Berger, J.O. (2010) Bayes and empirical-bayes multiplicity adjustment in the variable-selection problem. Ann. Stat., 38, 2587-2619.
    • (2010) Ann. Stat , vol.38 , pp. 2587-2619
    • Scott, J.G.1    Berger, J.O.2
  • 32
    • 40749094910 scopus 로고    scopus 로고
    • Bayesian ranking of biochemical system models
    • Vyshemirsky, V. and Girolami, M. (2008) Bayesian ranking of biochemical system models. Bioinformatics, 24, 833-839.
    • (2008) Bioinformatics , vol.24 , pp. 833-839
    • Vyshemirsky, V.1    Girolami, M.2
  • 33
    • 33749825955 scopus 로고    scopus 로고
    • Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical Gaussian models and Bayesian networks
    • Werhli, A. 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.1
  • 34
    • 77953839617 scopus 로고    scopus 로고
    • Inferring signaling pathway topologies from multiple perturbation measurements of specific biochemical species
    • Xu, T.-R. et al. (2010) Inferring signaling pathway topologies from multiple perturbation measurements of specific biochemical species. Sci. Signal., 3, ra20.
    • (2010) Sci. Signal , pp. 3-20
    • Xu, T.-R.1
  • 35
    • 0002817906 scopus 로고
    • On assessing prior distributions and Bayesian regression analysis with g-prior distributions
    • Goel, P.K. and Zellner, A. (eds) North-Holland, Amsterdam
    • Zellner, A. (1986) On assessing prior distributions and Bayesian regression analysis with g-prior distributions. In: Goel, P.K. and Zellner, A. (eds) Bayesian inference and decision techniques-Essays in honor of Bruno de Finetti, North-Holland, Amsterdam. pp. 233-243.
    • (1986) Bayesian Inference and Decision Techniques-Essays in Honor of Bruno de Finetti , pp. 233-243
    • Zellner, A.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.