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Items: 1 to 20 of 78

1.

Gene order computation using Alzheimer's DNA microarray gene expression data and the Ant Colony Optimisation algorithm.

Pang C, Jiang G, Wang S, Hu B, Liu Q, Deng Y, Huang X.

Int J Data Min Bioinform. 2012;6(6):617-32.

PMID:
23356011
2.

Assessment of gene order computing methods for Alzheimer's disease.

Hu B, Jiang G, Pang C, Wang S, Liu Q, Chen Z, Vanderburg CR, Rogers JT, Deng Y, Huang X.

BMC Med Genomics. 2013;6 Suppl 1:S8. doi: 10.1186/1755-8794-6-S1-S8. Epub 2013 Jan 23.

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.

4.

A multi-stage approach to clustering and imputation of gene expression profiles.

Wong DS, Wong FK, Wood GR.

Bioinformatics. 2007 Apr 15;23(8):998-1005. Epub 2007 Feb 18.

PMID:
17308340
5.

Clustering and re-clustering for pattern discovery in gene expression data.

Ma PC, Chan KC, Chiu DK.

J Bioinform Comput Biol. 2005 Apr;3(2):281-301.

PMID:
15852506
6.

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.

PMID:
15647300
7.

Incorporating biological knowledge into distance-based clustering analysis of microarray gene expression data.

Huang D, Pan W.

Bioinformatics. 2006 May 15;22(10):1259-68. Epub 2006 Feb 24.

PMID:
16500932
8.

Reordering based integrative expression profiling for microarray classification.

Wu X, Huang H, Sonachalam M, Reinhard S, Shen J, Pandey R, Chen JY.

BMC Bioinformatics. 2012 Mar 13;13 Suppl 2:S1. doi: 10.1186/1471-2105-13-S2-S1.

9.

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.

PMID:
16078363
10.

Dynamic range-based distance measure for microarray expressions and a fast gene-ordering algorithm.

Ray SS, Bandyopadhyay S, Pal SK.

IEEE Trans Syst Man Cybern B Cybern. 2007 Jun;37(3):742-9.

PMID:
17550128
11.
12.
13.

Spot detection and image segmentation in DNA microarray data.

Qin L, Rueda L, Ali A, Ngom A.

Appl Bioinformatics. 2005;4(1):1-11. Review.

PMID:
16000008
14.

A special local clustering algorithm for identifying the genes associated with Alzheimer's disease.

Pang CY, Hu W, Hu BQ, Shi Y, Vanderburg CR, Rogers JT, Huang X.

IEEE Trans Nanobioscience. 2010 Mar;9(1):44-50. doi: 10.1109/TNB.2009.2037745. Epub 2010 Jan 19.

15.

Weighted rank aggregation of cluster validation measures: a Monte Carlo cross-entropy approach.

Pihur V, Datta S, Datta S.

Bioinformatics. 2007 Jul 1;23(13):1607-15. Epub 2007 May 5.

PMID:
17483500
16.

Graph-based consensus clustering for class discovery from gene expression data.

Yu Z, Wong HS, Wang H.

Bioinformatics. 2007 Nov 1;23(21):2888-96. Epub 2007 Sep 14.

PMID:
17872912
17.

A general framework for biclustering gene expression data.

Li H, Chen X, Zhang K, Jiang T.

J Bioinform Comput Biol. 2006 Aug;4(4):911-33.

PMID:
17007074
18.

Biclustering of microarray data with MOSPO based on crowding distance.

Liu J, Li Z, Hu X, Chen Y.

BMC Bioinformatics. 2009 Apr 29;10 Suppl 4:S9. doi: 10.1186/1471-2105-10-S4-S9.

19.

An iterative data mining approach for mining overlapping coexpression patterns in noisy gene expression data.

Ma PC, Chan KC.

IEEE Trans Nanobioscience. 2009 Sep;8(3):252-8. doi: 10.1109/TNB.2009.2026747. Epub 2009 Jul 14.

PMID:
19605326
20.

Combinatorial optimization models for finding genetic signatures from gene expression datasets.

Berretta R, Costa W, Moscato P.

Methods Mol Biol. 2008;453:363-77. doi: 10.1007/978-1-60327-429-6_19.

PMID:
18712314

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