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Items: 1 to 20 of 122

1.

A decision-theory approach to interpretable set analysis for high-dimensional data.

Boca SM, Bravo HC, Caffo B, Leek JT, Parmigiani G.

Biometrics. 2013 Sep;69(3):614-23. doi: 10.1111/biom.12060. Epub 2013 Aug 2.

3.

Multiple-testing strategy for analyzing cDNA array data on gene expression.

Delongchamp RR, Bowyer JF, Chen JJ, Kodell RL.

Biometrics. 2004 Sep;60(3):774-82.

PMID:
15339301
4.

Parametric and nonparametric FDR estimation revisited.

Wu B, Guan Z, Zhao H.

Biometrics. 2006 Sep;62(3):735-44.

PMID:
16984315
5.
6.
7.

An empirical Bayesian approach for identifying differential coexpression in high-throughput experiments.

Dawson JA, Kendziorski C.

Biometrics. 2012 Jun;68(2):455-65. doi: 10.1111/j.1541-0420.2011.01688.x. Epub 2011 Oct 17.

8.

Testing the prediction error difference between 2 predictors.

van de Wiel MA, Berkhof J, van Wieringen WN.

Biostatistics. 2009 Jul;10(3):550-60. doi: 10.1093/biostatistics/kxp011. Epub 2009 Apr 20.

PMID:
19380517
9.

Bayesian designs and the control of frequentist characteristics: a practical solution.

Ventz S, Trippa L.

Biometrics. 2015 Mar;71(1):218-26. doi: 10.1111/biom.12226. Epub 2014 Sep 5.

PMID:
25196832
10.

Network-based empirical Bayes methods for linear models with applications to genomic data.

Li C, Wei Z, Li H.

J Biopharm Stat. 2010 Mar;20(2):209-22. doi: 10.1080/10543400903572712.

11.

Empirical Bayes screening of many p-values with applications to microarray studies.

Datta S, Datta S.

Bioinformatics. 2005 May 1;21(9):1987-94. Epub 2005 Feb 2.

PMID:
15691856
12.
13.

Sources of variation in false discovery rate estimation include sample size, correlation, and inherent differences between groups.

Zhang J, Coombes KR.

BMC Bioinformatics. 2012;13 Suppl 13:S1. doi: 10.1186/1471-2105-13-S13-S1. Epub 2012 Aug 24.

14.

Estimating the false discovery rate using nonparametric deconvolution.

van de Wiel MA, Kim KI.

Biometrics. 2007 Sep;63(3):806-15.

PMID:
17825012
15.

Wavelet thresholding with bayesian false discovery rate control.

Tadesse MG, Ibrahim JG, Vannucci M, Gentleman R.

Biometrics. 2005 Mar;61(1):25-35.

PMID:
15737075
16.

Comparison of seven methods for producing Affymetrix expression scores based on False Discovery Rates in disease profiling data.

Shedden K, Chen W, Kuick R, Ghosh D, Macdonald J, Cho KR, Giordano TJ, Gruber SB, Fearon ER, Taylor JM, Hanash S.

BMC Bioinformatics. 2005 Feb 10;6:26.

17.

An omnibus consistent adaptive percentile modified Wilcoxon rank sum test with applications in gene expression studies.

Thas O, Clement L, Rayner JC, Carvalho B, Van Criekinge W.

Biometrics. 2012 Jun;68(2):446-54. doi: 10.1111/j.1541-0420.2012.01750.x. Epub 2012 Apr 16.

PMID:
22506868
18.

Analysis of high dimensional data using pre-defined set and subset information, with applications to genomic data.

Guo W, Yang M, Xing C, Peddada SD.

BMC Bioinformatics. 2012 Jul 24;13:177. doi: 10.1186/1471-2105-13-177.

19.

A SATS algorithm for jointly identifying multiple differentially expressed gene sets.

Yang TY.

Stat Med. 2011 Jul 20;30(16):2028-39. doi: 10.1002/sim.4235. Epub 2011 Apr 7.

PMID:
21472762
20.

A Bayesian decision procedure for testing multiple hypotheses in DNA microarray experiments.

Gómez-Villegas MA, Salazar I, Sanz L.

Stat Appl Genet Mol Biol. 2014 Feb;13(1):49-65. doi: 10.1515/sagmb-2012-0076.

PMID:
24317791

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