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Items: 1 to 20 of 93

1.

EXPANDER--an integrative program suite for microarray data analysis.

Shamir R, Maron-Katz A, Tanay A, Linhart C, Steinfeld I, Sharan R, Shiloh Y, Elkon R.

BMC Bioinformatics. 2005 Sep 21;6:232.

2.

CLICK and EXPANDER: a system for clustering and visualizing gene expression data.

Sharan R, Maron-Katz A, Shamir R.

Bioinformatics. 2003 Sep 22;19(14):1787-99.

PMID:
14512350
3.

PATIKAmad: putting microarray data into pathway context.

Babur O, Colak R, Demir E, Dogrusoz U.

Proteomics. 2008 Jun;8(11):2196-8. doi: 10.1002/pmic.200700769.

PMID:
18452226
4.

GeneTools--application for functional annotation and statistical hypothesis testing.

Beisvag V, Jünge FK, Bergum H, Jølsum L, Lydersen S, Günther CC, Ramampiaro H, Langaas M, Sandvik AK, Laegreid A.

BMC Bioinformatics. 2006 Oct 24;7:470.

5.

Array2BIO: from microarray expression data to functional annotation of co-regulated genes.

Loots GG, Chain PS, Mabery S, Rasley A, Garcia E, Ovcharenko I.

BMC Bioinformatics. 2006 Jun 16;7:307.

6.

M@IA: a modular open-source application for microarray workflow and integrative datamining.

Le Béchec A, Zindy P, Sierocinski T, Petritis D, Bihouée A, Le Meur N, Léger J, Théret N.

In Silico Biol. 2008;8(1):63-9.

PMID:
18430991
7.

An interactive power analysis tool for microarray hypothesis testing and generation.

Seo J, Gordish-Dressman H, Hoffman EP.

Bioinformatics. 2006 Apr 1;22(7):808-14. Epub 2006 Jan 17.

PMID:
16418236
8.

Integrative Array Analyzer: a software package for analysis of cross-platform and cross-species microarray data.

Pan F, Kamath K, Zhang K, Pulapura S, Achar A, Nunez-Iglesias J, Huang Y, Yan X, Han J, Hu H, Xu M, Hu J, Zhou XJ.

Bioinformatics. 2006 Jul 1;22(13):1665-7. Epub 2006 May 3.

PMID:
16672260
9.

Design and evaluation of Actichip, a thematic microarray for the study of the actin cytoskeleton.

Muller J, Mehlen A, Vetter G, Yatskou M, Muller A, Chalmel F, Poch O, Friederich E, Vallar L.

BMC Genomics. 2007 Aug 29;8:294.

11.

High-Throughput GoMiner, an 'industrial-strength' integrative gene ontology tool for interpretation of multiple-microarray experiments, with application to studies of Common Variable Immune Deficiency (CVID).

Zeeberg BR, Qin H, Narasimhan S, Sunshine M, Cao H, Kane DW, Reimers M, Stephens RM, Bryant D, Burt SK, Elnekave E, Hari DM, Wynn TA, Cunningham-Rundles C, Stewart DM, Nelson D, Weinstein JN.

BMC Bioinformatics. 2005 Jul 5;6:168.

12.

I/NI-calls for the exclusion of non-informative genes: a highly effective filtering tool for microarray data.

Talloen W, Clevert DA, Hochreiter S, Amaratunga D, Bijnens L, Kass S, Göhlmann HW.

Bioinformatics. 2007 Nov 1;23(21):2897-902. Epub 2007 Oct 5.

PMID:
17921172
13.

GEM-TREND: a web tool for gene expression data mining toward relevant network discovery.

Feng C, Araki M, Kunimoto R, Tamon A, Makiguchi H, Niijima S, Tsujimoto G, Okuno Y.

BMC Genomics. 2009 Sep 3;10:411. doi: 10.1186/1471-2164-10-411.

14.

Cross-species and cross-platform gene expression studies with the Bioconductor-compliant R package 'annotationTools'.

Kuhn A, Luthi-Carter R, Delorenzi M.

BMC Bioinformatics. 2008 Jan 17;9:26. doi: 10.1186/1471-2105-9-26.

15.
16.

geneCBR: a translational tool for multiple-microarray analysis and integrative information retrieval for aiding diagnosis in cancer research.

Glez-Peña D, Díaz F, Hernández JM, Corchado JM, Fdez-Riverola F.

BMC Bioinformatics. 2009 Jun 18;10:187. doi: 10.1186/1471-2105-10-187.

17.

Microarray analysis of gene expression: considerations in data mining and statistical treatment.

Verducci JS, Melfi VF, Lin S, Wang Z, Roy S, Sen CK.

Physiol Genomics. 2006 May 16;25(3):355-63. Epub 2006 Mar 22. Review.

18.

GEPAS, a web-based tool for microarray data analysis and interpretation.

Tárraga J, Medina I, Carbonell J, Huerta-Cepas J, Minguez P, Alloza E, Al-Shahrour F, Vegas-Azcárate S, Goetz S, Escobar P, Garcia-Garcia F, Conesa A, Montaner D, Dopazo J.

Nucleic Acids Res. 2008 Jul 1;36(Web Server issue):W308-14. doi: 10.1093/nar/gkn303. Epub 2008 May 28.

19.

A modified hyperplane clustering algorithm allows for efficient and accurate clustering of extremely large datasets.

Sharma A, Podolsky R, Zhao J, McIndoe RA.

Bioinformatics. 2009 May 1;25(9):1152-7. doi: 10.1093/bioinformatics/btp123. Epub 2009 Mar 4.

20.

Clustering microarray gene expression data using weighted Chinese restaurant process.

Qin ZS.

Bioinformatics. 2006 Aug 15;22(16):1988-97. Epub 2006 Jun 9.

PMID:
16766561

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