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Items: 1 to 20 of 117

1.

Application of gene shaving and mixture models to cluster microarray gene expression data.

Do KA, McLachlan GJ, Bean R, Wen S.

Cancer Inform. 2007;5:25-43. Epub 2007 Apr 2.

2.

A mixture model-based approach to the clustering of microarray expression data.

McLachlan GJ, Bean RW, Peel D.

Bioinformatics. 2002 Mar;18(3):413-22.

PMID:
11934740
3.

Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays.

Alon U, Barkai N, Notterman DA, Gish K, Ybarra S, Mack D, Levine AJ.

Proc Natl Acad Sci U S A. 1999 Jun 8;96(12):6745-50.

4.

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.

5.

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

Jörnsten R, Yu B.

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

PMID:
12801870
6.

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.

7.

'Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns.

Hastie T, Tibshirani R, Eisen MB, Alizadeh A, Levy R, Staudt L, Chan WC, Botstein D, Brown P.

Genome Biol. 2000;1(2):RESEARCH0003. Epub 2000 Aug 4.

9.

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.

PMID:
16675467
10.

Tumor classification by partial least squares using microarray gene expression data.

Nguyen DV, Rocke DM.

Bioinformatics. 2002 Jan;18(1):39-50.

PMID:
11836210
11.

Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes.

Jiang H, Deng Y, Chen HS, Tao L, Sha Q, Chen J, Tsai CJ, Zhang S.

BMC Bioinformatics. 2004 Jun 24;5:81.

12.
13.

Prognostically useful gene-expression profiles in acute myeloid leukemia.

Valk PJ, Verhaak RG, Beijen MA, Erpelinck CA, Barjesteh van Waalwijk van Doorn-Khosrovani S, Boer JM, Beverloo HB, Moorhouse MJ, van der Spek PJ, Löwenberg B, Delwel R.

N Engl J Med. 2004 Apr 15;350(16):1617-28.

14.

Unsupervised fuzzy pattern discovery in gene expression data.

Wu GP, Chan KC, Wong AK.

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

15.

Clustering of gene expression data via normal mixture models.

McLachlan GJ, Flack LK, Ng SK, Wang K.

Methods Mol Biol. 2013;972:103-19. doi: 10.1007/978-1-60327-337-4_7.

PMID:
23385534
16.
17.

Between-group analysis of microarray data.

Culhane AC, Perrière G, Considine EC, Cotter TG, Higgins DG.

Bioinformatics. 2002 Dec;18(12):1600-8.

PMID:
12490444
18.

NIFTI: an evolutionary approach for finding number of clusters in microarray data.

Jonnalagadda S, Srinivasan R.

BMC Bioinformatics. 2009 Jan 30;10:40. doi: 10.1186/1471-2105-10-40.

19.

Cluster-Rasch models for microarray gene expression data.

Li H, Hong F.

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

20.

Partition decoupling for multi-gene analysis of gene expression profiling data.

Braun R, Leibon G, Pauls S, Rockmore D.

BMC Bioinformatics. 2011 Dec 30;12:497. doi: 10.1186/1471-2105-12-497.

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