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Items: 1 to 20 of 86

1.

Robust PCA based method for discovering differentially expressed genes.

Liu JX, Wang YT, Zheng CH, Sha W, Mi JX, Xu Y.

BMC Bioinformatics. 2013;14 Suppl 8:S3. doi: 10.1186/1471-2105-14-S8-S3. Epub 2013 May 9.

2.

Principal components analysis based methodology to identify differentially expressed genes in time-course microarray data.

Jonnalagadda S, Srinivasan R.

BMC Bioinformatics. 2008 Jun 6;9:267. doi: 10.1186/1471-2105-9-267.

3.

Nonparametric methods for identifying differentially expressed genes in microarray data.

Troyanskaya OG, Garber ME, Brown PO, Botstein D, Altman RB.

Bioinformatics. 2002 Nov;18(11):1454-61.

PMID:
12424116
4.

Highly expressed genes in pancreatic ductal adenocarcinomas: a comprehensive characterization and comparison of the transcription profiles obtained from three major technologies.

Iacobuzio-Donahue CA, Ashfaq R, Maitra A, Adsay NV, Shen-Ong GL, Berg K, Hollingsworth MA, Cameron JL, Yeo CJ, Kern SE, Goggins M, Hruban RH.

Cancer Res. 2003 Dec 15;63(24):8614-22.

5.

A P-Norm Robust Feature Extraction Method for Identifying Differentially Expressed Genes.

Liu J, Liu JX, Gao YL, Kong XZ, Wang XS, Wang D.

PLoS One. 2015 Jul 22;10(7):e0133124. doi: 10.1371/journal.pone.0133124. eCollection 2015.

6.

Robust PCA and classification in biosciences.

Hubert M, Engelen S.

Bioinformatics. 2004 Jul 22;20(11):1728-36. Epub 2004 Feb 26.

PMID:
14988110
7.

RPCA-Based Tumor Classification Using Gene Expression Data.

Liu JX, Xu Y, Zheng CH, Kong H, Lai ZH.

IEEE/ACM Trans Comput Biol Bioinform. 2015 Jul-Aug;12(4):964-70. doi: 10.1109/TCBB.2014.2383375.

PMID:
26357336
8.

Cross platform microarray analysis for robust identification of differentially expressed genes.

Bosotti R, Locatelli G, Healy S, Scacheri E, Sartori L, Mercurio C, Calogero R, Isacchi A.

BMC Bioinformatics. 2007 Mar 8;8 Suppl 1:S5.

9.

Complementary hierarchical clustering.

Nowak G, Tibshirani R.

Biostatistics. 2008 Jul;9(3):467-83. Epub 2007 Dec 18.

10.

MIClique: An algorithm to identify differentially coexpressed disease gene subset from microarray data.

Zhang H, Song X, Wang H, Zhang X.

J Biomed Biotechnol. 2009;2009:642524. doi: 10.1155/2009/642524. Epub 2010 Jan 20.

11.

ADGO: analysis of differentially expressed gene sets using composite GO annotation.

Nam D, Kim SB, Kim SK, Yang S, Kim SY, Chu IS.

Bioinformatics. 2006 Sep 15;22(18):2249-53. Epub 2006 Jul 12.

PMID:
16837524
12.

Multivariate approach for selecting sets of differentially expressed genes.

Chilingaryan A, Gevorgyan N, Vardanyan A, Jones D, Szabo A.

Math Biosci. 2002 Mar;176(1):59-69.

PMID:
11867084
13.

Identifying differentially expressed genes from microarray experiments via statistic synthesis.

Yang YH, Xiao Y, Segal MR.

Bioinformatics. 2005 Apr 1;21(7):1084-93. Epub 2004 Oct 28.

PMID:
15513985
14.

A simulation-based approach for evaluating microarray analyses.

Blades NJ, Grimshaw SD, Pendleton CR.

Biostatistics. 2010 Jul;11(3):533-6. doi: 10.1093/biostatistics/kxq010. Epub 2010 Feb 24.

PMID:
20181614
15.

A novel data mining method to identify assay-specific signatures in functional genomic studies.

Rollins DK, Zhai D, Joe AL, Guidarelli JW, Murarka A, Gonzalez R.

BMC Bioinformatics. 2006 Aug 14;7:377.

16.

Adaptive filtering of microarray gene expression data based on Gaussian mixture decomposition.

Marczyk M, Jaksik R, Polanski A, Polanska J.

BMC Bioinformatics. 2013 Mar 20;14:101. doi: 10.1186/1471-2105-14-101.

17.

Identification of differentially expressed gene categories in microarray studies using nonparametric multivariate analysis.

Nettleton D, Recknor J, Reecy JM.

Bioinformatics. 2008 Jan 15;24(2):192-201. Epub 2007 Nov 27.

PMID:
18042553
18.
19.

Discovering gene expression patterns in time course microarray experiments by ANOVA-SCA.

Nueda MJ, Conesa A, Westerhuis JA, Hoefsloot HC, Smilde AK, Talón M, Ferrer A.

Bioinformatics. 2007 Jul 15;23(14):1792-800. Epub 2007 May 22.

PMID:
17519250
20.

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