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Volumn 21, Issue 6, 2005, Pages 754-764

An empirical Bayes approach to inferring large-scale gene association networks

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BAYES THEOREM; BREAST CANCER; COMPUTER PROGRAM; COMPUTER SIMULATION; CORRELATION ANALYSIS; DNA MICROARRAY; GENE EXPRESSION; GENE IDENTIFICATION; GENETIC ALGORITHM; HUMAN; MATHEMATICAL COMPUTING; NORMAL DISTRIBUTION; PRIORITY JOURNAL; SENSITIVITY AND SPECIFICITY; VALIDATION PROCESS;

EID: 15944364151     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/bti062     Document Type: Article
Times cited : (621)

References (56)
  • 1
    • 0023440913 scopus 로고
    • Molecular cloning of two CD7 (T-cell leukemia antigen) cDNAs by a COS cell expression system
    • Aruffo,A. and Seed,B. (1983) Molecular cloning of two CD7 (T-cell leukemia antigen) cDNAs by a COS cell expression system. EMBO J., 6, 3313-3316.
    • (1983) EMBO J. , vol.6 , pp. 3313-3316
    • Aruffo, A.1    Seed, B.2
  • 2
    • 0742305866 scopus 로고    scopus 로고
    • Network biology: Understanding the cell's functional organization
    • Barabási,A.-L. (2004) Network biology: understanding the cell's functional organization. Nat. Rev. Genet., 5, 101-113.
    • (2004) Nat. Rev. Genet. , vol.5 , pp. 101-113
    • Barabási, A.-L.1
  • 3
    • 0042125048 scopus 로고    scopus 로고
    • Revising regulatory networks: From expression data to linear causal models
    • Bay,S.D., Shrager,J., Pohorille,A. and Langley,P. (2002) Revising regulatory networks: from expression data to linear causal models. J. Biomed. Informatics, 35, 298-297.
    • (2002) J. Biomed. Informatics , vol.35 , pp. 297-298
    • Bay, S.D.1    Shrager, J.2    Pohorille, A.3    Langley, P.4
  • 4
    • 0001677717 scopus 로고
    • Controlling the false discovery rate: A practical and powerful approach to multiple testing
    • Benjamini,Y. and Hochberg,Y. (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J.R. Statist. Soc. B, 57, 289-300.
    • (1995) J.R. Statist. Soc. B , vol.57 , pp. 289-300
    • Benjamini, Y.1    Hochberg, Y.2
  • 5
    • 0034370950 scopus 로고    scopus 로고
    • The adaptive control of the false discovery rate in multiple hypotheses testing
    • Benjamini,Y. and Hochberg,Y. (2000) The adaptive control of the false discovery rate in multiple hypotheses testing. J. Behav. Educ. Statist., 25, 60-83.
    • (2000) J. Behav. Educ. Statist. , vol.25 , pp. 60-83
    • Benjamini, Y.1    Hochberg, Y.2
  • 6
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • Breiman,L. (1996) Bagging predictors. Machine Learn., 24, 123-140.
    • (1996) Machine Learn. , vol.24 , pp. 123-140
    • Breiman, L.1
  • 8
    • 21344487864 scopus 로고
    • Tests of linearity, multivariate normality and the adequacy of linear scores
    • Cox,D.R. and Wermuth,N. (1994) Tests of linearity, multivariate normality and the adequacy of linear scores. Appl. Stat. 43, 347-355.
    • (1994) Appl. Stat. , vol.43 , pp. 347-355
    • Cox, D.R.1    Wermuth, N.2
  • 9
    • 0034354940 scopus 로고    scopus 로고
    • Graphical interaction models for multivariate time series
    • Dahlhaus,R. (2000) Graphical interaction models for multivariate time series. Metrika, 51, 157-172.
    • (2000) Metrika , vol.51 , pp. 157-172
    • Dahlhaus, R.1
  • 10
    • 0043130707 scopus 로고    scopus 로고
    • Inferring gene regulatory networks from time-ordered gene expression data of Bacillus subtilis using differential equations
    • De Hoon,M.J.L., Imoto,S., Kobayashi,K., Ogasawara,N. and Miyano,S. (2003) Inferring gene regulatory networks from time-ordered gene expression data of Bacillus subtilis using differential equations. Pac. Symp. Biocomput., 8, 17-28.
    • (2003) Pac. Symp. Biocomput. , vol.8 , pp. 17-28
    • De Hoon, M.J.L.1    Imoto, S.2    Kobayashi, K.3    Ogasawara, N.4    Miyano, S.5
  • 11
    • 12344321571 scopus 로고    scopus 로고
    • Discovery of meaningful associations in genomic data using partial correlation coefficients
    • de la Fuente,A., Bing,N., Hoeschele,I. and Mendes,P. (2004) Discovery of meaningful associations in genomic data using partial correlation coefficients. Bioinformatics, 20, 3575-3582.
    • (2004) Bioinformatics , vol.20 , pp. 3575-3582
    • de la Fuente, A.1    Bing, N.2    Hoeschele, I.3    Mendes, P.4
  • 12
    • 0001038826 scopus 로고
    • Covariance selection
    • Dempster,A.P. (1972) Covariance selection. Biometrics, 28, 157-175.
    • (1972) Biometrics , vol.28 , pp. 157-175
    • Dempster, A.P.1
  • 13
    • 0033736476 scopus 로고    scopus 로고
    • Genetic network inference: From co-expression clustering to reverse engineering
    • D'haeseleer,P., Liang,S. and Somogyi,R. (2000) Genetic network inference: from co-expression clustering to reverse engineering. Bioinformatics, 16, 707-726.
    • (2000) Bioinformatics , vol.16 , pp. 707-726
    • D'haeseleer, P.1    Liang, S.2    Somogyi, R.3
  • 14
    • 15944399178 scopus 로고    scopus 로고
    • Sparse graphical models for exploring gene expression data
    • Dobra,A., Hans,C., Jones,B., Nevins,J.R. and West,M. (2004) Sparse graphical models for exploring gene expression data. J. Multiv. Anal., 90, 196-212.
    • (2004) J. Multiv. Anal. , vol.90 , pp. 196-212
    • Dobra, A.1    Hans, C.2    Jones, B.3    Nevins, J.R.4    West, M.5
  • 15
    • 15944403709 scopus 로고    scopus 로고
    • Model selection for Gaussian concentration graphs
    • Drton,M. and Perlman,M.D. (2004) Model selection for Gaussian concentration graphs. Biometrika, 91, 591-602.
    • (2004) Biometrika , vol.91 , pp. 591-602
    • Drton, M.1    Perlman, M.D.2
  • 17
    • 0038364283 scopus 로고    scopus 로고
    • Robbins, empirical Bayes, and microarrays
    • Efron,B. (2003) Robbins, empirical Bayes, and microarrays. Ann. Statist., 31, 366-378.
    • (2003) Ann. Statist. , vol.31 , pp. 366-378
    • Efron, B.1
  • 18
    • 2142732441 scopus 로고    scopus 로고
    • Large-scale simultaneous hypothesis testing: The choice of a null hypothesis
    • Efron,B. (2004) Large-scale simultaneous hypothesis testing: the choice of a null hypothesis. J. Am. Statist. Assoc., 99, 96-104.
    • (2004) J. Am. Statist. Assoc. , vol.99 , pp. 96-104
    • Efron, B.1
  • 20
    • 84887916087 scopus 로고
    • Regularized discriminant analysis
    • Friedman,J.H. (1989) Regularized discriminant analysis. J. Am. Statist. Assoc., 84, 165-175.
    • (1989) J. Am. Statist. Assoc. , vol.84 , pp. 165-175
    • Friedman, J.H.1
  • 21
    • 0842288337 scopus 로고    scopus 로고
    • Inferring cellular networks using probabilistics graphical models
    • Friedman,N. (2004) Inferring cellular networks using probabilistics graphical models. Science, 303, 799-805.
    • (2004) Science , vol.303 , pp. 799-805
    • Friedman, N.1
  • 22
    • 0037262841 scopus 로고    scopus 로고
    • Being Bayesian about network structure. A Bayesian approach to structure discovery in Bayesian networks
    • Friedman,N. and Koller,D. (2003) Being Bayesian about network structure. A Bayesian approach to structure discovery in Bayesian networks. Machine Learn., 50, 95-125.
    • (2003) Machine Learn. , vol.50 , pp. 95-125
    • Friedman, N.1    Koller, D.2
  • 23
    • 0033707946 scopus 로고    scopus 로고
    • Using Bayesian networks to analyze gene expression data
    • Friedman,N., Linial,M., Nachman.I. and Pe'er,D. (2000) Using Bayesian networks to analyze gene 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    Pe'er, D.4
  • 24
    • 15944419638 scopus 로고    scopus 로고
    • Efficient quadratic regularization for expression arrays
    • Hastie,T. and Tibshirani,T. (2004) Efficient quadratic regularization for expression arrays. Biostatistics, 5, 329-340.
    • (2004) Biostatistics , vol.5 , pp. 329-340
    • Hastie, T.1    Tibshirani, T.2
  • 26
    • 0000988073 scopus 로고
    • New light on the correlation coefficient and its transforms
    • Hotelling,H. (1953) New light on the correlation coefficient and its transforms. J. R. Statist. Soc. B, 15, 193-232.
    • (1953) J. R. Statist. Soc. B , vol.15 , pp. 193-232
    • Hotelling, H.1
  • 27
    • 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
  • 29
    • 0038806692 scopus 로고    scopus 로고
    • Identification, characterization, and function of a novel oncogene: The peripheral cannabinoid receptor CB2
    • Jorda,M.A., Rayman,N., Valk,P., De Wee,E. and Delwel,R. (2003) Identification, characterization, and function of a novel oncogene: the peripheral cannabinoid receptor CB2. Ann. NY Acad. Sci. 996, 10-16.
    • (2003) Ann. NY Acad. Sci. , vol.996 , pp. 10-16
    • Jorda, M.A.1    Rayman, N.2    Valk, P.3    De Wee, E.4    Delwel, R.5
  • 30
    • 0034570871 scopus 로고    scopus 로고
    • Correspondence analysis of genes and tissue types and finding genetic links from microarray data
    • Kishino,H. and Waddell,P.J. (2000) Correspondence analysis of genes and tissue types and finding genetic links from microarray data. Genome Informatics, 11, 83-95.
    • (2000) Genome Informatics , vol.11 , pp. 83-95
    • Kishino, H.1    Waddell, P.J.2
  • 31
    • 0004047518 scopus 로고    scopus 로고
    • Oxford University Press, Oxford
    • Lauritzen,S. (1996) Graphical Models. Oxford University Press, Oxford.
    • (1996) Graphical Models
    • Lauritzen, S.1
  • 35
    • 0013288412 scopus 로고    scopus 로고
    • Dynamic Bayesian networks: Representation, inference and learning
    • PhD Thesis, Computer Science Division, University of California, Berkeley, CA
    • Murphy,K.P. (2002) Dynamic Bayesian networks: representation, inference and learning. PhD Thesis, Computer Science Division, University of California, Berkeley, CA.
    • (2002)
    • Murphy, K.P.1
  • 36
    • 84947145047 scopus 로고
    • A generalized inverse for matrices
    • Penrose,R. (1955) A generalized inverse for matrices. Proc. Cambridge Phil. Soc., 51, 406-413.
    • (1955) Proc. Cambridge Phil. Soc. , vol.51 , pp. 406-413
    • Penrose, R.1
  • 38
    • 0032042805 scopus 로고    scopus 로고
    • Expected classification error of the Fisher linear classifier with pseudoinverse covariance matrix
    • Raudys,S. and Duin,R.P.W. (1998) Expected classification error of the Fisher linear classifier with pseudoinverse covariance matrix. Pattern Recogn. Lett., 19, 385-392.
    • (1998) Pattern Recogn. Lett. , vol.19 , pp. 385-392
    • Raudys, S.1    Duin, R.P.W.2
  • 39
    • 0037028488 scopus 로고    scopus 로고
    • MLL3, a new human member of the TRX/MLL gene family, maps to 7q36, a chromosome region frequently deleted in myeloid leukemia
    • Ruault,M., Brun,M.E., Ventura,M., Roizes,G. and De Sario,A. (2002) MLL3, a new human member of the TRX/MLL gene family, maps to 7q36, a chromosome region frequently deleted in myeloid leukemia. Gene, 284, 73-81.
    • (2002) Gene , vol.284 , pp. 73-81
    • Ruault, M.1    Brun, M.E.2    Ventura, M.3    Roizes, G.4    De Sario, A.5
  • 40
    • 0003758190 scopus 로고    scopus 로고
    • Estimating the posterior probability of differential gene expression from microarray data
    • Poster presentation, Jackson Laboratory, Bar Harbor
    • Sapir,M. and Churchill,G.A. (2000) Estimating the posterior probability of differential gene expression from microarray data. Poster presentation, Jackson Laboratory, Bar Harbor.
    • (2000)
    • Sapir, M.1    Churchill, G.A.2
  • 41
    • 0037941585 scopus 로고    scopus 로고
    • Module networks: Identifying regulatory modules and their condition-specific regulators from gene expression data
    • Segal,E., Shapira,M., Regev,A., Pe'er,D., Botstein,D., Koller,D. and Friedman,N. (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    Shapira, M.2    Regev, A.3    Pe'er, D.4    Botstein, D.5    Koller, D.6    Friedman, N.7
  • 42
    • 0036080160 scopus 로고    scopus 로고
    • Bagging, boosting and the random subspace method for linear classifiers
    • Skurichina,M. and Duin,R.P.W. (2002) Bagging, boosting and the random subspace method for linear classifiers. Pattern Anal. Appl. 5, 121-135.
    • (2002) Pattern Anal. Appl. , vol.5 , pp. 121-135
    • Skurichina, M.1    Duin, R.P.W.2
  • 43
    • 0036020892 scopus 로고    scopus 로고
    • A direct approach to false discovery rates
    • Storey,J.D. (2002) A direct approach to false discovery rates. J.R. Statist. Soc. B, 64, 479-498.
    • (2002) J.R. Statist. Soc. B , vol.64 , pp. 479-498
    • Storey, J.D.1
  • 44
    • 0042424602 scopus 로고    scopus 로고
    • Statistical significance for genome-wide experiments
    • Storey,J.D. and Tibshirani,R. (2003) Statistical significance for genome-wide experiments. Proc. Natl Acad. Sci. USA, 100, 9440-9445.
    • (2003) Proc. Natl. Acad. Sci. USA , vol.100 , pp. 9440-9445
    • Storey, J.D.1    Tibshirani, R.2
  • 45
    • 0036191190 scopus 로고    scopus 로고
    • Inference of a genetic network by a combined approach of cluster analysis and graphical Gaussian modeling
    • Toh,H. and Horimoto,K. (2002a) Inference of a genetic network by a combined approach of cluster analysis and graphical Gaussian modeling. Bioinformatics, 18, 287-297.
    • (2002) Bioinformatics , vol.18 , pp. 287-297
    • Toh, H.1    Horimoto, K.2
  • 46
    • 0036035791 scopus 로고    scopus 로고
    • System for automatically inferring a genetic network from expression profiles
    • Toh,H. and Horimoto,K. (2002b) System for automatically inferring a genetic network from expression profiles. J. Biol. Phys. 28, 449-464.
    • (2002) J. Biol. Phys. , vol.28 , pp. 449-464
    • Toh, H.1    Horimoto, K.2
  • 49
    • 0034575477 scopus 로고    scopus 로고
    • Cluster inferences methods and graphical models evaluated on NC160 microarray gene expression data
    • Waddell,P.J. and Kishino,H. (2000) Cluster inferences methods and graphical models evaluated on NC160 microarray gene expression data. Genome Informatics, 11, 129-140.
    • (2000) Genome Informatics , vol.11 , pp. 129-140
    • Waddell, P.J.1    Kishino, H.2
  • 50
    • 0345327603 scopus 로고    scopus 로고
    • MGraph: Graphical model for microarray data analysis
    • Wang,J., Myklebost,O. and Hovig,E. (2003) MGraph: graphical model for microarray data analysis. Bioinformatics, 19, 2210-2211.
    • (2003) Bioinformatics , vol.19 , pp. 2210-2211
    • Wang, J.1    Myklebost, O.2    Hovig, E.3
  • 51
  • 54
    • 3843149220 scopus 로고    scopus 로고
    • Efficient estimation of covariance selection models
    • Wong,F., Carter,C.K. and Kohn,R. (2003) Efficient estimation of covariance selection models. Biometrika, 90, 809-830.
    • (2003) Biometrika , vol.90 , pp. 809-830
    • Wong, F.1    Carter, C.K.2    Kohn, R.3
  • 56
    • 0037197936 scopus 로고    scopus 로고
    • Reverse engineering gene networks using singular value decomposition and robust regression
    • Yeung,M.K.S., Tegnér,J. and Collins,J.J. (2002) Reverse engineering gene networks using singular value decomposition and robust regression. Proc. Natl Acad. Sci. USA, 99, 6163-6168.
    • (2002) Proc. Natl. Acad. Sci. USA , vol.99 , pp. 6163-6168
    • Yeung, M.K.S.1    Tegnér, J.2    Collins, J.J.3


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