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Items: 1 to 20 of 124

1.

Bayesian robust inference for differential gene expression in microarrays with multiple samples.

Gottardo R, Raftery AE, Yeung KY, Bumgarner RE.

Biometrics. 2006 Mar;62(1):10-8.

PMID:
16542223
2.

Intensity-based hierarchical Bayes method improves testing for differentially expressed genes in microarray experiments.

Sartor MA, Tomlinson CR, Wesselkamper SC, Sivaganesan S, Leikauf GD, Medvedovic M.

BMC Bioinformatics. 2006 Dec 19;7:538.

3.

Statistical analysis of microarray data: a Bayesian approach.

Gottardo R, Pannucci JA, Kuske CR, Brettin T.

Biostatistics. 2003 Oct;4(4):597-620.

4.
5.

A semiparametric Bayesian approach for estimating the gene expression distribution.

Zou F, Huang H, Ibrahim JG.

J Biopharm Stat. 2010 Mar;20(2):267-80. doi: 10.1080/10543400903572746.

6.

A Bayesian mixture model for partitioning gene expression data.

Zhou C, Wakefield J.

Biometrics. 2006 Jun;62(2):515-25.

PMID:
16918916
7.

Bayesian hierarchical error model for analysis of gene expression data.

Cho H, Lee JK.

Bioinformatics. 2004 Sep 1;20(13):2016-25. Epub 2004 Mar 25.

8.

Bayesian modeling of differential gene expression.

Lewin A, Richardson S, Marshall C, Glazier A, Aitman T.

Biometrics. 2006 Mar;62(1):1-9.

PMID:
16542224
9.

A Laplace mixture model for identification of differential expression in microarray experiments.

Bhowmick D, Davison AC, Goldstein DR, Ruffieux Y.

Biostatistics. 2006 Oct;7(4):630-41. Epub 2006 Mar 24.

10.

Gene selection: a Bayesian variable selection approach.

Lee KE, Sha N, Dougherty ER, Vannucci M, Mallick BK.

Bioinformatics. 2003 Jan;19(1):90-7.

11.

A flexible and powerful bayesian hierarchical model for ChIP-Chip experiments.

Gottardo R, Li W, Johnson WE, Liu XS.

Biometrics. 2008 Jun;64(2):468-78. Epub 2007 Sep 20.

PMID:
17888037
12.

Bayesian neural networks for bivariate binary data: an application to prostate cancer study.

Chakraborty S, Ghosh M, Maiti T, Tewari A.

Stat Med. 2005 Dec 15;24(23):3645-62.

PMID:
16138362
13.

Flexible empirical Bayes models for differential gene expression.

Lo K, Gottardo R.

Bioinformatics. 2007 Feb 1;23(3):328-35. Epub 2006 Nov 30.

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16.

β-empirical Bayes inference and model diagnosis of microarray data.

Mollah MM, Mollah MN, Kishino H.

BMC Bioinformatics. 2012 Jun 19;13:135. doi: 10.1186/1471-2105-13-135.

17.

Bayesian meta-analysis models for microarray data: a comparative study.

Conlon EM, Song JJ, Liu A.

BMC Bioinformatics. 2007 Mar 7;8:80.

18.

Identifying differentially expressed genes in meta-analysis via Bayesian model-based clustering.

Jung YY, Oh MS, Shin DW, Kang SH, Oh HS.

Biom J. 2006 Jun;48(3):435-50.

PMID:
16845907
19.

Comparative evaluation of gene-set analysis methods.

Liu Q, Dinu I, Adewale AJ, Potter JD, Yasui Y.

BMC Bioinformatics. 2007 Nov 7;8:431.

20.

A Bayesian network classification methodology for gene expression data.

Helman P, Veroff R, Atlas SR, Willman C.

J Comput Biol. 2004;11(4):581-615.

PMID:
15579233
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