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Items: 1 to 20 of 127

1.

GENESHIFT: a nonparametric approach for integrating microarray gene expression data based on the inner product as a distance measure between the distributions of genes.

Lazar C, Taminau J, Meganck S, Steenhoff D, Coletta A, Solís DY, Molter C, Duque R, Bersini H, Nowé A.

IEEE/ACM Trans Comput Biol Bioinform. 2013 Mar-Apr;10(2):383-92. doi: 10.1109/TCBB.2013.12.

PMID:
23929862
2.

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.

3.

The effects of normalization on the correlation structure of microarray data.

Qiu X, Brooks AI, Klebanov L, Yakovlev N.

BMC Bioinformatics. 2005 May 16;6:120.

4.

A GMM-IG framework for selecting genes as expression panel biomarkers.

Wang M, Chen JY.

Artif Intell Med. 2010 Feb-Mar;48(2-3):75-82. doi: 10.1016/j.artmed.2009.07.006. Epub 2009 Dec 8.

PMID:
20004087
5.

A novel approach to the clustering of microarray data via nonparametric density estimation.

De Bin R, Risso D.

BMC Bioinformatics. 2011 Feb 8;12:49. doi: 10.1186/1471-2105-12-49.

6.

Empirical comparison of cross-platform normalization methods for gene expression data.

Rudy J, Valafar F.

BMC Bioinformatics. 2011 Dec 7;12:467. doi: 10.1186/1471-2105-12-467.

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9.

YuGene: a simple approach to scale gene expression data derived from different platforms for integrated analyses.

Lê Cao KA, Rohart F, McHugh L, Korn O, Wells CA.

Genomics. 2014 Apr;103(4):239-51. doi: 10.1016/j.ygeno.2014.03.001. Epub 2014 Mar 22.

PMID:
24667244
10.

Quadratic regression analysis for gene discovery and pattern recognition for non-cyclic short time-course microarray experiments.

Liu H, Tarima S, Borders AS, Getchell TV, Getchell ML, Stromberg AJ.

BMC Bioinformatics. 2005 Apr 25;6:106.

11.

In silico microdissection of microarray data from heterogeneous cell populations.

Lähdesmäki H, Shmulevich L, Dunmire V, Yli-Harja O, Zhang W.

BMC Bioinformatics. 2005 Mar 14;6:54.

12.

The statistics of identifying differentially expressed genes in Expresso and TM4: a comparison.

Sioson AA, Mane SP, Li P, Sha W, Heath LS, Bohnert HJ, Grene R.

BMC Bioinformatics. 2006 Apr 20;7:215.

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15.

Fold change rank ordering statistics: a new method for detecting differentially expressed genes.

Dembélé D, Kastner P.

BMC Bioinformatics. 2014 Jan 15;15:14. doi: 10.1186/1471-2105-15-14.

16.

Batch effect removal methods for microarray gene expression data integration: a survey.

Lazar C, Meganck S, Taminau J, Steenhoff D, Coletta A, Molter C, Weiss-Solís DY, Duque R, Bersini H, Nowé A.

Brief Bioinform. 2013 Jul;14(4):469-90. doi: 10.1093/bib/bbs037. Epub 2012 Jul 31.

17.

A statistical framework for integrating two microarray data sets in differential expression analysis.

Lai Y, Eckenrode SE, She JX.

BMC Bioinformatics. 2009 Jan 30;10 Suppl 1:S23. doi: 10.1186/1471-2105-10-S1-S23.

18.

A comparison of batch effect removal methods for enhancement of prediction performance using MAQC-II microarray gene expression data.

Luo J, Schumacher M, Scherer A, Sanoudou D, Megherbi D, Davison T, Shi T, Tong W, Shi L, Hong H, Zhao C, Elloumi F, Shi W, Thomas R, Lin S, Tillinghast G, Liu G, Zhou Y, Herman D, Li Y, Deng Y, Fang H, Bushel P, Woods M, Zhang J.

Pharmacogenomics J. 2010 Aug;10(4):278-91. doi: 10.1038/tpj.2010.57.

19.

Comparison of Affymetrix data normalization methods using 6,926 experiments across five array generations.

Autio R, Kilpinen S, Saarela M, Kallioniemi O, Hautaniemi S, Astola J.

BMC Bioinformatics. 2009 Jan 30;10 Suppl 1:S24. doi: 10.1186/1471-2105-10-S1-S24.

20.

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. Epub 2011 Mar 16.

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