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Results: 1 to 20 of 154

Similar articles for PubMed (Select 18986519)

1.

Gene set analyses for interpreting microarray experiments on prokaryotic organisms.

Tintle NL, Best AA, DeJongh M, Van Bruggen D, Heffron F, Porwollik S, Taylor RC.

BMC Bioinformatics. 2008 Nov 5;9:469. doi: 10.1186/1471-2105-9-469.

2.

Multivariate analysis of variance test for gene set analysis.

Tsai CA, Chen JJ.

Bioinformatics. 2009 Apr 1;25(7):897-903. doi: 10.1093/bioinformatics/btp098. Epub 2009 Mar 2.

3.

A meta-data based method for DNA microarray imputation.

Jörnsten R, Ouyang M, Wang HY.

BMC Bioinformatics. 2007 Mar 29;8:109.

4.

Comparative evaluation of gene-set analysis methods.

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

BMC Bioinformatics. 2007 Nov 7;8:431.

5.

GAGE: generally applicable gene set enrichment for pathway analysis.

Luo W, Friedman MS, Shedden K, Hankenson KD, Woolf PJ.

BMC Bioinformatics. 2009 May 27;10:161. doi: 10.1186/1471-2105-10-161.

6.

PAGE: parametric analysis of gene set enrichment.

Kim SY, Volsky DJ.

BMC Bioinformatics. 2005 Jun 8;6:144.

7.

SEGS: search for enriched gene sets in microarray data.

Trajkovski I, Lavrac N, Tolar J.

J Biomed Inform. 2008 Aug;41(4):588-601. doi: 10.1016/j.jbi.2007.12.001. Epub 2007 Dec 15.

8.

Differential analysis of DNA microarray gene expression data.

Hatfield GW, Hung SP, Baldi P.

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

PMID:
12581345
9.

Estimation of gene induction enables a relevance-based ranking of gene sets.

Bartholomé K, Kreutz C, Timmer J.

J Comput Biol. 2009 Jul;16(7):959-67. doi: 10.1089/cmb.2008.0226.

PMID:
19580524
10.

Comparative study of gene set enrichment methods.

Abatangelo L, Maglietta R, Distaso A, D'Addabbo A, Creanza TM, Mukherjee S, Ancona N.

BMC Bioinformatics. 2009 Sep 2;10:275. doi: 10.1186/1471-2105-10-275.

11.

Improving gene set analysis of microarray data by SAM-GS.

Dinu I, Potter JD, Mueller T, Liu Q, Adewale AJ, Jhangri GS, Einecke G, Famulski KS, Halloran P, Yasui Y.

BMC Bioinformatics. 2007 Jul 5;8:242.

12.

Overview of mRNA expression profiling using DNA microarrays.

Katagiri F, Glazebrook J.

Curr Protoc Mol Biol. 2009 Jan;Chapter 22:Unit 22.4. doi: 10.1002/0471142727.mb2204s85.

PMID:
19170027
13.

Hierarchical Bayes models for cDNA microarray gene expression.

Lönnstedt I, Britton T.

Biostatistics. 2005 Apr;6(2):279-91.

14.

Overview of mRNA expression profiling using microarrays.

Katagiri F, Glazebrook J.

Curr Protoc Mol Biol. 2004 Sep;Chapter 22:Unit 22.4. doi: 10.1002/0471142727.mb2204s67.

PMID:
18265348
15.

A framework for determining outlying microarray experiments.

Wan R, Wheelock AM, Mamitsuka H.

Genome Inform. 2008;20:64-76.

16.
17.

Identifying set-wise differential co-expression in gene expression microarray data.

Cho SB, Kim J, Kim JH.

BMC Bioinformatics. 2009 Apr 16;10:109. doi: 10.1186/1471-2105-10-109.

18.

Comparison of small n statistical tests of differential expression applied to microarrays.

Murie C, Woody O, Lee AY, Nadon R.

BMC Bioinformatics. 2009 Feb 3;10:45. doi: 10.1186/1471-2105-10-45.

19.

Determining gene expression on a single pair of microarrays.

Reid RW, Fodor AA.

BMC Bioinformatics. 2008 Nov 21;9:489. doi: 10.1186/1471-2105-9-489.

20.

[Comparison of statistical methods for detecting differential expression in microarray data].

Shan WJ, Tong CF, Shi JS.

Yi Chuan. 2008 Dec;30(12):1640-6. Chinese.

PMID:
19073583
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