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Volumn 9, Issue , 2008, Pages

Bayesian models and meta analysis for multiple tissue gene expression data following corticosteroid administration

Author keywords

[No Author keywords available]

Indexed keywords

95% CREDIBLE INTERVALS; BAYESIAN MIXTURE MODEL; DEVIANCE INFORMATION CRITERION; DIFFERENTIALLY EXPRESSED GENE; GENE EXPRESSION DATA; HIERARCHICAL BAYESIAN; NUMBER OF COMPONENTS; POSTERIOR PROBABILITY;

EID: 55449121996     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-9-354     Document Type: Article
Times cited : (10)

References (40)
  • 1
    • 0030711655 scopus 로고    scopus 로고
    • Meta-analysis: Principles and procedures
    • 2127866 9432250
    • Egger M Davey SG Phillips AN Meta-analysis: Principles and procedures British Medical Journal 1997, 315:1371-1374. 2127866 9432250
    • (1997) British Medical Journal , vol.315 , pp. 1371-1374
    • Egger, M.1    Davey, S.G.2    Phillips, A.N.3
  • 2
    • 0030742003 scopus 로고    scopus 로고
    • The promise and problems of meta-analysis
    • 10.1056/NEJM199708213370810 9262502
    • Bailar JC The promise and problems of meta-analysis New England Journal of Medicine 1997, 337:559-61. 10.1056/NEJM199708213370810 9262502
    • (1997) New England Journal of Medicine , vol.337 , pp. 559-561
    • Bailar, J.C.1
  • 3
    • 0000499514 scopus 로고
    • Bayes methods for combining the results of cancer studies in humans and other species
    • 10.2307/2288631
    • DuMouchel WH Harris JE Bayes methods for combining the results of cancer studies in humans and other species Journal of the American Statistical Association 1983, 78:293-315. 10.2307/2288631
    • (1983) Journal of the American Statistical Association , vol.78 , pp. 293-315
    • DuMouchel, W.H.1    Harris, J.E.2
  • 4
    • 0029612242 scopus 로고
    • Bayesian approaches to random-effects meta-analysis: A comparative study
    • 10.1002/sim.4780142408 8619108
    • Smith TC Spiegelhalter DJ Thomas A Bayesian approaches to random-effects meta-analysis: A comparative study Stat Med 1995, 14:2685-2699. 10.1002/ sim.4780142408 8619108
    • (1995) Stat Med , vol.14 , pp. 2685-2699
    • Smith, T.C.1    Spiegelhalter, D.J.2    Thomas, A.3
  • 5
    • 1542309545 scopus 로고    scopus 로고
    • Statistical issues and methods for meta-analysis of microarray data: A case study in prostate cancer
    • 10.1007/s10142-003-0087-5 12884057
    • Ghosh D Barette T Rhodes D Statistical issues and methods for meta-analysis of microarray data: A case study in prostate cancer Functional Integrative Genomics 2003, 3:180-188. 10.1007/ s10142-003-0087-5 12884057
    • (2003) Functional Integrative Genomics , vol.3 , pp. 180-188
    • Ghosh, D.1    Barette, T.2    Rhodes, D.3
  • 7
    • 40549120695 scopus 로고    scopus 로고
    • A Parametric Joint Model of DNA-Protein Binding, Gene Expression and DNA Sequence Data to Detect Target Genes of a Transcription Factor
    • 18229708
    • Pan W Wei W Khodursky A A Parametric Joint Model of DNA-Protein Binding, Gene Expression and DNA Sequence Data to Detect Target Genes of a Transcription Factor Pac Symp Biocomput 2008:465-476. 18229708
    • (2008) Pac Symp Biocomput , pp. 465-476
    • Pan, W.1    Wei, W.2    Khodursky, A.3
  • 8
    • 34147103409 scopus 로고    scopus 로고
    • Bayesian meta-analysis models for microarray data: A comparative study
    • 1851021 17343745 10.1186/1471-2105-8-80
    • Conlon EM Song JJ Liu A Bayesian meta-analysis models for microarray data: A comparative study BMC Bioinformatics 2007, 8:80. 1851021 17343745 10.1186/1471-2105-8-80
    • (2007) BMC Bioinformatics , vol.8 , pp. 80
    • Conlon, E.M.1    Song, J.J.2    Liu, A.3
  • 9
    • 18744398140 scopus 로고    scopus 로고
    • Hierarchical Bayesian Neural Network for Gene Expression Temporal Patterns
    • Article 20
    • Liang Y Kelemen A Hierarchical Bayesian Neural Network for Gene Expression Temporal Patterns Stat Appl Genet Mol Biol 2004, 3. Article 20
    • (2004) Stat Appl Genet Mol Biol , vol.3
    • Liang, Y.1    Kelemen, A.2
  • 10
    • 38049053725 scopus 로고    scopus 로고
    • Temporal Gene Expression Classification with Regularised Neural Network
    • 10.1504/IJBRA.2005.008443
    • Liang Y Kelemen A Temporal Gene Expression Classification with Regularised Neural Network International Journal of Bioinformatics Research and Applications 2005, 1(4):399-413. 10.1504/IJBRA.2005.008443
    • (2005) International Journal of Bioinformatics Research and Applications , vol.1 , Issue.4 , pp. 399-413
    • Liang, Y.1    Kelemen, A.2
  • 11
    • 21444457867 scopus 로고    scopus 로고
    • Differential and Trajectory Methods for Time Course Gene Expression Data
    • 10.1093/bioinformatics/bti465
    • Liang Y Tayo B Cai X Kelemen A Differential and Trajectory Methods for Time Course Gene Expression Data Bioinformatics 2005, 20(13):3009-3016. 10.1093/bioinformatics/bti465
    • (2005) Bioinformatics , vol.20 , Issue.13 , pp. 3009-3016
    • Liang, Y.1    Tayo, B.2    Cai, X.3    Kelemen, A.4
  • 12
    • 29544434311 scopus 로고    scopus 로고
    • Associating phenotypes with molecular events: A review of statistical advances and challenges underpinning microarray analyses
    • 10.1007/s10142-005-0006-z
    • Liang Y Kelemen A Associating phenotypes with molecular events: A review of statistical advances and challenges underpinning microarray analyses Journal of Functional and Integrative Genomics 2006, 6:1-13. 10.1007/ s10142-005-0006-z
    • (2006) Journal of Functional and Integrative Genomics , vol.6 , pp. 1-13
    • Liang, Y.1    Kelemen, A.2
  • 13
    • 84886757216 scopus 로고    scopus 로고
    • Bayesian State Space Model for Inferring and Predicting Transcription Profiles in Gene Expression
    • Liang Y Kelemen A Bayesian State Space Model for Inferring and Predicting Transcription Profiles in Gene Expression Biometrical Journals 2007, 49(3):1-14.
    • (2007) Biometrical Journals , vol.49 , Issue.3 , pp. 1-14
    • Liang, Y.1    Kelemen, A.2
  • 15
    • 2142854576 scopus 로고    scopus 로고
    • A mixture Model approach to detecting differentially expressed genes with microarray data
    • 10.1007/s10142-003-0085-7
    • Pan W Lin J A mixture Model approach to detecting differentially expressed genes with microarray data Functional and Integrative Genomics 2003, 3:117-124. 10.1007/s10142-003-0085-7
    • (2003) Functional and Integrative Genomics , vol.3 , pp. 117-124
    • Pan, W.1    Lin, J.2
  • 16
    • 8844236882 scopus 로고    scopus 로고
    • A mixture model-based strategy for selecting sets of genes in multiclass response microarray experiments
    • 10.1093/bioinformatics/bth285 15117756
    • Broet P Lewin A Richardson S Dalmasso C Magdelenat H A mixture model-based strategy for selecting sets of genes in multiclass response microarray experiments Bioinformatics 2004, 20:2562-2571. 10.1093/ bioinformatics/bth285 15117756
    • (2004) Bioinformatics , vol.20 , pp. 2562-2571
    • Broet, P.1    Lewin, A.2    Richardson, S.3    Dalmasso, C.4    Magdelenat, H.5
  • 17
    • 2942651196 scopus 로고    scopus 로고
    • Modeling Microarray data using a threshold mixture model
    • 10.1111/j.0006-341X.2004.00182.x 15180663
    • Kauermann G Eilers P Modeling Microarray data using a threshold mixture model Biometrics 2004, 60:376-387. 10.1111/j.0006-341X.2004.00182.x 15180663
    • (2004) Biometrics , vol.60 , pp. 376-387
    • Kauermann, G.1    Eilers, P.2
  • 18
    • 8844258766 scopus 로고    scopus 로고
    • A mixture Model approach for estimating the local false discovery rate in DNA microarray analysis
    • 10.1093/bioinformatics/bth310 15145810
    • Liao J Lian Y Selvanayagam Z Shih W A mixture Model approach for estimating the local false discovery rate in DNA microarray analysis Bioinformatics 2004, 20(16):2694-2701. 10.1093/bioinformatics/bth310 15145810
    • (2004) Bioinformatics , vol.20 , Issue.16 , pp. 2694-2701
    • Liao, J.1    Lian, Y.2    Selvanayagam, Z.3    Shih, W.4
  • 19
    • 4444305034 scopus 로고    scopus 로고
    • Mixture models for assessing differential expression in complex tissues using microarray data
    • PMID:14988124 14988124
    • Ghosh D Mixture models for assessing differential expression in complex tissues using microarray data Bioinformatics 2004. PMID:14988124 14988124
    • (2004) Bioinformatics
    • Ghosh, D.1
  • 20
    • 0242391946 scopus 로고    scopus 로고
    • In vivo Multi-Tissue Corticosteroid Microarray Time Series
    • 10.1517/phgs.4.6.791.22816 14596642
    • Almon RR Chen J Snyder G DuBois DC Jusko WJ Hoffman E In vivo Multi-Tissue Corticosteroid Microarray Time Series Pharmacogenomics 2003, 4:791-799. 10.1517/phgs.4.6.791.22816 14596642
    • (2003) Pharmacogenomics , vol.4 , pp. 791-799
    • Almon, R.R.1    Chen, J.2    Snyder, G.3    DuBois, D.C.4    Jusko, W.J.5    Hoffman, E.6
  • 21
    • 0141742369 scopus 로고    scopus 로고
    • Modeling of corticosteroid pharmacogenomics in rat liver using gene microarrays
    • 10.1124/jpet.103.053256
    • Jin JY Almon RR Dubois DC Jusko WJ Modeling of corticosteroid pharmacogenomics in rat liver using gene microarrays Journal of Pharmaceutical Experiment. Therory 2003, 307(1):93-109. 10.1124/ jpet.103.053256
    • (2003) Journal of Pharmaceutical Experiment. Therory , vol.307 , Issue.1 , pp. 93-109
    • Jin, J.Y.1    Almon, R.R.2    Dubois, D.C.3    Jusko, W.J.4
  • 23
    • 28944434535 scopus 로고    scopus 로고
    • Bayesian Inference for Categorical Data Analysis
    • 10.1007/s10260-005-0121-y
    • Agresti A Hitchcock DB Bayesian Inference for Categorical Data Analysis Statistical Methods and Applications 2005, 14:297-330. 10.1007/ s10260-005-0121-y
    • (2005) Statistical Methods and Applications , vol.14 , pp. 297-330
    • Agresti, A.1    Hitchcock, D.B.2
  • 25
    • 0035998835 scopus 로고    scopus 로고
    • Model-Based Clustering, Discriminant analysis, and Density estimation
    • 10.1198/016214502760047131
    • Fraley C Raftery A Model-Based Clustering, Discriminant analysis, and Density estimation Journal of American Statistical Association 2002, 97(458):611-631. 10.1198/016214502760047131
    • (2002) Journal of American Statistical Association , vol.97 , Issue.458 , pp. 611-631
    • Fraley, C.1    Raftery, A.2
  • 26
    • 0036203115 scopus 로고    scopus 로고
    • A mixture model-based approach to the clustering of microarray expression data
    • 10.1093/bioinformatics/18.3.413 11934740
    • McLachlan GJ Bean RW Peel D A mixture model-based approach to the clustering of microarray expression data Bioinformatics 2002, 18:413-422. 10.1093/bioinformatics/18.3.413 11934740
    • (2002) Bioinformatics , vol.18 , pp. 413-422
    • McLachlan, G.J.1    Bean, R.W.2    Peel, D.3
  • 27
    • 0036739286 scopus 로고    scopus 로고
    • Bayesian infinite mixture model based clustering of gene expression profiles
    • 10.1093/bioinformatics/18.9.1194 12217911
    • Medvedovic M Sivaganesan S Bayesian infinite mixture model based clustering of gene expression profiles Bioinformatics 2002, 18:1194-1206. 10.1093/bioinformatics/18.9.1194 12217911
    • (2002) Bioinformatics , vol.18 , pp. 1194-1206
    • Medvedovic, M.1    Sivaganesan, S.2
  • 28
    • 21444450507 scopus 로고    scopus 로고
    • A variational bayesian mixture modelling framework for cluster analysis of gene-expression data
    • 10.1093/bioinformatics/bti466 15860564
    • Teschendorff AE Wang Y Barbosa-Morais NL Brenton JD Caldas C A variational bayesian mixture modelling framework for cluster analysis of gene-expression data Bioinformatics 2005, 21:3025-3033. 10.1093/ bioinformatics/bti466 15860564
    • (2005) Bioinformatics , vol.21 , pp. 3025-3033
    • Teschendorff, A.E.1    Wang, Y.2    Barbosa-Morais, N.L.3    Brenton, J.D.4    Caldas, C.5
  • 29
    • 0006407254 scopus 로고    scopus 로고
    • WinBUGS - A Bayesian modelling framework: Concepts, structure, and extensibility
    • 10.1023/A:1008929526011
    • Lunn DJ Thomas A Best N Spiegelhalter D WinBUGS - a Bayesian modelling framework: Concepts, structure, and extensibility Statistics and Computing 2000, 10:325-337. 10.1023/A:1008929526011
    • (2000) Statistics and Computing , vol.10 , pp. 325-337
    • Lunn, D.J.1    Thomas, A.2    Best, N.3    Spiegelhalter, D.4
  • 31
    • 18444401138 scopus 로고    scopus 로고
    • Bayesian mixture model for differential gene expression
    • 10.1111/j.1467-9876.2005.05593.x
    • Do KA Muller P Tang F Bayesian mixture model for differential gene expression Journal of the Royal Statistical Society, Series C 2005, 54(3):627-644. 10.1111/j.1467-9876.2005.05593.x
    • (2005) Journal of the Royal Statistical Society, Series C , vol.54 , Issue.3 , pp. 627-644
    • Do, K.A.1    Muller, P.2    Tang, F.3
  • 32
    • 33845734547 scopus 로고    scopus 로고
    • Variable selection in clustering via Dirichlet process mixture models
    • 10.1093/biomet/93.4.877
    • Kim S Tadesee MG Vannucci M Variable selection in clustering via Dirichlet process mixture models Biometrika 2006, 93(4):877-893. 10.1093/ biomet/93.4.877
    • (2006) Biometrika , vol.93 , Issue.4 , pp. 877-893
    • Kim, S.1    Tadesee, M.G.2    Vannucci, M.3
  • 34
    • 33644855951 scopus 로고    scopus 로고
    • A data-driven clustering method for time course gene expression data
    • 1388097 16510852 10.1093/nar/gkl013
    • Ma P Castillo-Davis CI Zhong W Liu JS A data-driven clustering method for time course gene expression data Nucleic Acids Research 2006, 34(4):1261-1269. 1388097 16510852 10.1093/nar/gkl013
    • (2006) Nucleic Acids Research , vol.34 , Issue.4 , pp. 1261-1269
    • Ma, P.1    Castillo-Davis, C.I.2    Zhong, W.3    Liu, J.S.4
  • 35
    • 0037339264 scopus 로고    scopus 로고
    • Clustering of time-course gene expression data using a mixed-effects model with B-spline
    • 10.1093/bioinformatics/btg014 12611802
    • Luan Y Li H Clustering of time-course gene expression data using a mixed-effects model with B-spline Bioinformatics 2003, 19:474-482. 10.1093/bioinformatics/btg014 12611802
    • (2003) Bioinformatics , vol.19 , pp. 474-482
    • Luan, Y.1    Li, H.2
  • 36
    • 1342288029 scopus 로고    scopus 로고
    • Model-based methods for identifying periodically regulated genes based on the time course microarray geneexpression data
    • 10.1093/bioinformatics/btg413 14960459
    • Luan Y Li H Model-based methods for identifying periodically regulated genes based on the time course microarray geneexpression data Bioinformatics 2004, 20:332-339. 10.1093/bioinformatics/btg413 14960459
    • (2004) Bioinformatics , vol.20 , pp. 332-339
    • Luan, Y.1    Li, H.2
  • 37
    • 0001287271 scopus 로고    scopus 로고
    • Regression shrinkage and selection via the lasso
    • Tibshirani R Regression shrinkage and selection via the lasso J Royal Statist Soc B 1996, 58(1):267-288.
    • (1996) J Royal Statist Soc B , vol.58 , Issue.1 , pp. 267-288
    • Tibshirani, R.1
  • 38
    • 33746154240 scopus 로고    scopus 로고
    • Doubly regularized support vector machine
    • Wang L Zhu J Zou H Doubly regularized support vector machine Statistica Sinica 2006, 16:589-615.
    • (2006) Statistica Sinica , vol.16 , pp. 589-615
    • Wang, L.1    Zhu, J.2    Zou, H.3
  • 39
    • 35348872772 scopus 로고    scopus 로고
    • Oracle and adaptive compound decision rules for false discovery rate control
    • 10.1198/016214507000000545
    • Sun W Cai T Oracle and adaptive compound decision rules for false discovery rate control J American Statistical Association 2007, 102:901-912. 10.1198/016214507000000545
    • (2007) J American Statistical Association , vol.102 , pp. 901-912
    • Sun, W.1    Cai, T.2
  • 40
    • 55449095941 scopus 로고    scopus 로고
    • Statistical Advances and Challenges for Analyzing Correlated High Dimensional SNP Data in Genomic Study for Complex Diseases
    • 10.1214/07-SS026
    • Liang Y Kelemen A Statistical Advances and Challenges for Analyzing Correlated High Dimensional SNP Data in Genomic Study for Complex Diseases Statistics Surveys 2008, 2:43-60. 10.1214/07-SS026
    • (2008) Statistics Surveys , vol.2 , pp. 43-60
    • Liang, Y.1    Kelemen, A.2


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