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Predictive response-relevant clustering of expression data provides insights into disease processes.

Hopcroft LE, McBride MW, Harris KJ, Sampson AK, McClure JD, Graham D, Young G, Holyoake TL, Girolami MA, Dominiczak AF.

Nucleic Acids Res. 2010 Nov;38(20):6831-40. doi: 10.1093/nar/gkq550. Epub 2010 Jun 22.


Simultaneous gene clustering and subset selection for sample classification via MDL.

Jörnsten R, Yu B.

Bioinformatics. 2003 Jun 12;19(9):1100-9.


Combining Pareto-optimal clusters using supervised learning for identifying co-expressed genes.

Maulik U, Mukhopadhyay A, Bandyopadhyay S.

BMC Bioinformatics. 2009 Jan 20;10:27. doi: 10.1186/1471-2105-10-27.


Effect of data normalization on fuzzy clustering of DNA microarray data.

Kim SY, Lee JW, Bae JS.

BMC Bioinformatics. 2006 Mar 14;7:134.


Response projected clustering for direct association with physiological and clinical response data.

Yi SG, Park T, Lee JK.

BMC Bioinformatics. 2008 Jan 31;9:76. doi: 10.1186/1471-2105-9-76.


Detecting clusters of different geometrical shapes in microarray gene expression data.

Kim DW, Lee KH, Lee D.

Bioinformatics. 2005 May 1;21(9):1927-34. Epub 2005 Jan 12.


Mixture models with multiple levels, with application to the analysis of multifactor gene expression data.

Jörnsten R, Keleş S.

Biostatistics. 2008 Jul;9(3):540-54. doi: 10.1093/biostatistics/kxm051. Epub 2008 Feb 5.


Classification of microarray data with factor mixture models.

Martella F.

Bioinformatics. 2006 Jan 15;22(2):202-8. Epub 2005 Nov 15.


Cluster-Rasch models for microarray gene expression data.

Li H, Hong F.

Genome Biol. 2001;2(8):RESEARCH0031. Epub 2001 Jul 31.


Network constrained clustering for gene microarray data.

Zhu D, Hero AO, Cheng H, Khanna R, Swaroop A.

Bioinformatics. 2005 Nov 1;21(21):4014-20. Epub 2005 Sep 1.


A mixture model with random-effects components for clustering correlated gene-expression profiles.

Ng SK, McLachlan GJ, Wang K, Ben-Tovim Jones L, Ng SW.

Bioinformatics. 2006 Jul 15;22(14):1745-52. Epub 2006 May 3.


Exploring matrix factorization techniques for significant genes identification of Alzheimer's disease microarray gene expression data.

Kong W, Mou X, Hu X.

BMC Bioinformatics. 2011;12 Suppl 5:S7. doi: 10.1186/1471-2105-12-S5-S7. Epub 2011 Jul 27.


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.


Construction, visualisation, and clustering of transcription networks from microarray expression data.

Freeman TC, Goldovsky L, Brosch M, van Dongen S, Mazière P, Grocock RJ, Freilich S, Thornton J, Enright AJ.

PLoS Comput Biol. 2007 Oct;3(10):2032-42.


LCE: a link-based cluster ensemble method for improved gene expression data analysis.

Iam-on N, Boongoen T, Garrett S.

Bioinformatics. 2010 Jun 15;26(12):1513-9. doi: 10.1093/bioinformatics/btq226. Epub 2010 May 5.


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.


Dynamic model-based clustering for time-course gene expression data.

Wu FX, Zhang WJ, Kusalik AJ.

J Bioinform Comput Biol. 2005 Aug;3(4):821-36.

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