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Items: 1 to 20 of 310

1.

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.

2.

Gene expression analysis in clear cell renal cell carcinoma using gene set enrichment analysis for biostatistical management.

Maruschke M, Reuter D, Koczan D, Hakenberg OW, Thiesen HJ.

BJU Int. 2011 Jul;108(2 Pt 2):E29-35. doi: 10.1111/j.1464-410X.2010.09794.x.

3.

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.

4.

A comparative study on gene-set analysis methods for assessing differential expression associated with the survival phenotype.

Lee S, Kim J, Lee S.

BMC Bioinformatics. 2011 Sep 26;12:377. doi: 10.1186/1471-2105-12-377.

5.

Concordant integrative gene set enrichment analysis of multiple large-scale two-sample expression data sets.

Lai Y, Zhang F, Nayak TK, Modarres R, Lee NH, McCaffrey TA.

BMC Genomics. 2014;15 Suppl 1:S6. doi: 10.1186/1471-2164-15-S1-S6.

6.

Extensions to gene set enrichment.

Jiang Z, Gentleman R.

Bioinformatics. 2007 Feb 1;23(3):306-13.

7.

Gene set enrichment meta-learning analysis: next- generation sequencing versus microarrays.

Stiglic G, Bajgot M, Kokol P.

BMC Bioinformatics. 2010 Apr 8;11:176. doi: 10.1186/1471-2105-11-176.

9.

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.

10.

A multivariate extension of the gene set enrichment analysis.

Klebanov L, Glazko G, Salzman P, Yakovlev A, Xiao Y.

J Bioinform Comput Biol. 2007 Oct;5(5):1139-53.

PMID:
17933015
11.

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.

12.
13.

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.

14.

A stochastic model for identifying differential gene pair co-expression patterns in prostate cancer progression.

Mo WJ, Fu XP, Han XT, Yang GY, Zhang JG, Guo FH, Huang Y, Mao YM, Li Y, Xie Y.

BMC Genomics. 2009 Jul 29;10:340. doi: 10.1186/1471-2164-10-340.

15.

Bi-directional gene set enrichment and canonical correlation analysis identify key diet-sensitive pathways and biomarkers of metabolic syndrome.

Morine MJ, McMonagle J, Toomey S, Reynolds CM, Moloney AP, Gormley IC, Gaora PO, Roche HM.

BMC Bioinformatics. 2010 Oct 7;11:499. doi: 10.1186/1471-2105-11-499.

17.

Gene set enrichment analysis made simple.

Irizarry RA, Wang C, Zhou Y, Speed TP.

Stat Methods Med Res. 2009 Dec;18(6):565-75. doi: 10.1177/0962280209351908. Erratum in: Stat Methods Med Res. 2011 Oct;20(5):571.

18.

Mining gene expression data by interpreting principal components.

Roden JC, King BW, Trout D, Mortazavi A, Wold BJ, Hart CE.

BMC Bioinformatics. 2006 Apr 7;7:194.

19.

Random forests-based differential analysis of gene sets for gene expression data.

Hsueh HM, Zhou DW, Tsai CA.

Gene. 2013 Apr 10;518(1):179-86. doi: 10.1016/j.gene.2012.11.034.

PMID:
23219997
20.

Gene set analysis using sufficient dimension reduction.

Hsueh HM, Tsai CA.

BMC Bioinformatics. 2016 Feb 6;17:74. doi: 10.1186/s12859-016-0928-6.

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