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Items: 1 to 20 of 99

1.

Gene expression data analysis using closed item set mining for labeled data.

Rotter A, Novak PK, Baebler S, Toplak N, Blejec A, Lavrac N, Gruden K.

OMICS. 2010 Apr;14(2):177-86. doi: 10.1089/omi.2009.0126.

2.

Microarray data mining using landmark gene-guided clustering.

Chopra P, Kang J, Yang J, Cho H, Kim HS, Lee MG.

BMC Bioinformatics. 2008 Feb 11;9:92. doi: 10.1186/1471-2105-9-92.

3.

Analysis of oligonucleotide array experiments with repeated measures using mixed models.

Li H, Wood CL, Getchell TV, Getchell ML, Stromberg AJ.

BMC Bioinformatics. 2004 Dec 30;5:209.

4.

High confidence rule mining for microarray analysis.

McIntosh T, Chawla S.

IEEE/ACM Trans Comput Biol Bioinform. 2007 Oct-Dec;4(4):611-23.

PMID:
17975272
5.

An entropy-based gene selection method for cancer classification using microarray data.

Liu X, Krishnan A, Mondry A.

BMC Bioinformatics. 2005 Mar 24;6:76.

7.

ILOOP--a web application for two-channel microarray interwoven loop design.

Pirooznia M, Gong P, Yang JY, Yang MQ, Perkins EJ, Deng Y.

BMC Genomics. 2008 Sep 16;9 Suppl 2:S11. doi: 10.1186/1471-2164-9-S2-S11.

8.

Krylov subspace algorithms for computing GeneRank for the analysis of microarray data mining.

Wu G, Zhang Y, Wei Y.

J Comput Biol. 2010 Apr;17(4):631-46. doi: 10.1089/cmb.2009.0004.

PMID:
20426695
9.

Inferential clustering approach for microarray experiments with replicated measurements.

Salicrú M, Vives S, Zheng T.

IEEE/ACM Trans Comput Biol Bioinform. 2009 Oct-Dec;6(4):594-604. doi: 10.1109/TCBB.2008.106.

PMID:
19875858
10.

Mining subspace clusters from DNA microarray data using large itemset techniques.

Chang YI, Chen JR, Tsai YC.

J Comput Biol. 2009 May;16(5):745-68. doi: 10.1089/cmb.2008.0161.

PMID:
19432542
11.

Microarray data analysis: from disarray to consolidation and consensus.

Allison DB, Cui X, Page GP, Sabripour M.

Nat Rev Genet. 2006 Jan;7(1):55-65. Review. Erratum in: Nat Rev Genet. 2006 May;7(5):406.

PMID:
16369572
12.

Statistical considerations for analysis of microarray experiments.

Owzar K, Barry WT, Jung SH.

Clin Transl Sci. 2011 Dec;4(6):466-77. doi: 10.1111/j.1752-8062.2011.00309.x. Epub 2011 Nov 7. Review.

13.

Identification of differentially expressed genes for time-course microarray data based on modified RM ANOVA.

ElBakry O, Ahmad MO, Swamy MN.

IEEE/ACM Trans Comput Biol Bioinform. 2012;9(2):451-66. doi: 10.1109/TCBB.2011.65. Epub 2011 Mar 30.

PMID:
21464508
14.
15.

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.

16.

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.

17.

CoPub Mapper: mining MEDLINE based on search term co-publication.

Alako BT, Veldhoven A, van Baal S, Jelier R, Verhoeven S, Rullmann T, Polman J, Jenster G.

BMC Bioinformatics. 2005 Mar 11;6:51.

18.

Gene microarray data analysis using parallel point-symmetry-based clustering.

Sarkar A, Maulik U.

Int J Data Min Bioinform. 2015;11(3):277-300.

PMID:
26333263
19.
20.

Identification of biomarker genes for resistance to a pathogen by a novel method for meta-analysis of single-channel microarray datasets.

Wójcik PI, Ouellet T, Balcerzak M, Dzwinel W.

J Bioinform Comput Biol. 2015 Aug;13(4):1550013. doi: 10.1142/S0219720015500134. Epub 2015 Mar 24.

PMID:
25903423

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