Format
Sort by
Items per page

Send to

Choose Destination

Links from PubMed

Items: 1 to 20 of 138

1.

An investigation of two multivariate permutation methods for controlling the false discovery proportion.

Korn EL, Li MC, McShane LM, Simon R.

Stat Med. 2007 Oct 30;26(24):4428-40.

PMID:
17357994
2.

An investigation on performance of Significance Analysis of Microarray (SAM) for the comparisons of several treatments with one control in the presence of small-variance genes.

Lin D, Shkedy Z, Burzykowski T, Ion R, Göhlmann HW, Bondt AD, Perer T, Geerts T, Van den Wyngaert I, Bijnens L.

Biom J. 2008 Oct;50(5):801-23. doi: 10.1002/bimj.200710467.

PMID:
18932139
3.

Determination of the differentially expressed genes in microarray experiments using local FDR.

Aubert J, Bar-Hen A, Daudin JJ, Robin S.

BMC Bioinformatics. 2004 Sep 6;5:125.

4.

Estimation of false discovery proportion under general dependence.

Pawitan Y, Calza S, Ploner A.

Bioinformatics. 2006 Dec 15;22(24):3025-31.

PMID:
17046978
5.
6.

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
7.

Sample size calculations for controlling the distribution of false discovery proportion in microarray experiments.

Oura T, Matsui S, Kawakami K.

Biostatistics. 2009 Oct;10(4):694-705. doi: 10.1093/biostatistics/kxp024.

PMID:
19628638
8.

Selection of differentially expressed genes in microarray data analysis.

Chen JJ, Wang SJ, Tsai CA, Lin CJ.

Pharmacogenomics J. 2007 Jun;7(3):212-20.

PMID:
16940966
9.

A two-step multiple comparison procedure for a large number of tests and multiple treatments.

Jiang H, Doerge RW.

Stat Appl Genet Mol Biol. 2006;5:Article28.

PMID:
17402912
10.
11.
12.

Differential analysis of DNA microarray gene expression data.

Hatfield GW, Hung SP, Baldi P.

Mol Microbiol. 2003 Feb;47(4):871-7. Review.

13.

Evaluation of a statistical equivalence test applied to microarray data.

Qiu J, Cui X.

J Biopharm Stat. 2010 Mar;20(2):240-66. doi: 10.1080/10543400903572738.

PMID:
20309757
14.

Support vector machine quantile regression for detecting differentially expressed genes in microarray analysis.

Sohn I, Kim S, Hwang C, Lee JW, Shim J.

Methods Inf Med. 2008;47(5):459-67.

PMID:
18852921
15.
16.
17.

Significance analysis of microarray for relative quantitation of LC/MS data in proteomics.

Roxas BA, Li Q.

BMC Bioinformatics. 2008 Apr 10;9:187. doi: 10.1186/1471-2105-9-187.

18.

D-Serine exposure resulted in gene expression changes implicated in neurodegenerative disorders and neuronal dysfunction in male Fischer 344 rats.

Davidson ME, Kerepesi LA, Soto A, Chan VT.

Arch Toxicol. 2009 Aug;83(8):747-62. doi: 10.1007/s00204-009-0405-3.

PMID:
19212759
19.

Empirical bayes methods and false discovery rates for microarrays.

Efron B, Tibshirani R.

Genet Epidemiol. 2002 Jun;23(1):70-86.

PMID:
12112249
20.

Filtering for increased power for microarray data analysis.

Hackstadt AJ, Hess AM.

BMC Bioinformatics. 2009 Jan 8;10:11. doi: 10.1186/1471-2105-10-11.

Supplemental Content

Support Center