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Items: 1 to 20 of 98

1.

Modeling intra-tumor protein expression heterogeneity in tissue microarray experiments.

Shen R, Ghosh D, Taylor JM.

Stat Med. 2008 May 20;27(11):1944-59. doi: 10.1002/sim.3217.

2.

Tissue microarray sampling strategy for prostate cancer biomarker analysis.

Rubin MA, Dunn R, Strawderman M, Pienta KJ.

Am J Surg Pathol. 2002 Mar;26(3):312-9.

PMID:
11859202
3.

Reconstructing tumor-wise protein expression in tissue microarray studies using a Bayesian cell mixture model.

Shen R, Taylor JM, Ghosh D.

Bioinformatics. 2008 Dec 15;24(24):2880-6. doi: 10.1093/bioinformatics/btn536. Epub 2008 Oct 14.

4.

A hierarchical Naïve Bayes Model for handling sample heterogeneity in classification problems: an application to tissue microarrays.

Demichelis F, Magni P, Piergiorgi P, Rubin MA, Bellazzi R.

BMC Bioinformatics. 2006 Nov 24;7:514.

5.

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
6.

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.

PMID:
15044230
7.

The tissue microarray data exchange specification: implementation by the Cooperative Prostate Cancer Tissue Resource.

Berman JJ, Datta M, Kajdacsy-Balla A, Melamed J, Orenstein J, Dobbin K, Patel A, Dhir R, Becich MJ.

BMC Bioinformatics. 2004 Feb 27;5:19.

9.

Semiparametric Bayesian modeling of random genetic effects in family-based association studies.

Zhang L, Mukherjee B, Hu B, Moreno V, Cooney KA.

Stat Med. 2009 Jan 15;28(1):113-39. doi: 10.1002/sim.3413.

10.

WDR19 expression is increased in prostate cancer compared with normal cells, but low-intensity expression in cancers is associated with shorter time to biochemical failures and local recurrence.

Lin B, Utleg AG, Gravdal K, White JT, Halvorsen OJ, Lu W, True LD, Vessella R, Lange PH, Nelson PS, Hood L, Kalland KH, Akslen LA.

Clin Cancer Res. 2008 Mar 1;14(5):1397-406. doi: 10.1158/1078-0432.CCR-07-1535.

11.

alpha-Methylacyl coenzyme A racemase as a tissue biomarker for prostate cancer.

Rubin MA, Zhou M, Dhanasekaran SM, Varambally S, Barrette TR, Sanda MG, Pienta KJ, Ghosh D, Chinnaiyan AM.

JAMA. 2002 Apr 3;287(13):1662-70.

PMID:
11926890
12.

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

Conlon EM, Song JJ, Liu A.

BMC Bioinformatics. 2007 Mar 7;8:80.

13.

Mixture models for single-cell assays with applications to vaccine studies.

Finak G, McDavid A, Chattopadhyay P, Dominguez M, De Rosa S, Roederer M, Gottardo R.

Biostatistics. 2014 Jan;15(1):87-101. doi: 10.1093/biostatistics/kxt024. Epub 2013 Jul 24.

14.

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.

15.

On the geometric modeling approach to empirical null distribution estimation for empirical Bayes modeling of multiple hypothesis testing.

Wu B.

Comput Biol Chem. 2013 Apr;43:17-22. doi: 10.1016/j.compbiolchem.2012.12.001. Epub 2012 Dec 22.

16.

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
17.

Partial least squares dimension reduction for microarray gene expression data with a censored response.

Nguyen DV.

Math Biosci. 2005 Jan;193(1):119-37. Epub 2005 Jan 22.

PMID:
15681279
18.

Weighted lasso in graphical Gaussian modeling for large gene network estimation based on microarray data.

Shimamura T, Imoto S, Yamaguchi R, Miyano S.

Genome Inform. 2007;19:142-53.

PMID:
18546512
19.
20.

Combining multiple microarray studies and modeling interstudy variation.

Choi JK, Yu U, Kim S, Yoo OJ.

Bioinformatics. 2003;19 Suppl 1:i84-90.

PMID:
12855442

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