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Items: 1 to 20 of 83

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.

5.

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.

6.

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
7.

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.

8.

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.

9.

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.

10.

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
11.

Nearest hyperplane distance neighbor clustering algorithm applied to gene co-expression analysis in Alzheimer's disease.

Pasluosta CF, Dua P, Lukiw WJ.

Conf Proc IEEE Eng Med Biol Soc. 2011;2011:5559-62. doi: 10.1109/IEMBS.2011.6091344.

12.

Clustering time-varying gene expression profiles using scale-space signals.

Syeda-Mahmood T.

Proc IEEE Comput Soc Bioinform Conf. 2003;2:48-56.

PMID:
16452778
13.

Inferential clustering approach for microarray experiments with replicated measurements.

SalicrĂș 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
14.
15.

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
16.

An adaptive meta-clustering approach: combining the information from different clustering results.

Zeng Y, Tang J, Garcia-Frias J, Gao GR.

Proc IEEE Comput Soc Bioinform Conf. 2002;1:276-87.

PMID:
15838144
17.
18.

Identification of coherent patterns in gene expression data using an efficient biclustering algorithm and parallel coordinate visualization.

Cheng KO, Law NF, Siu WC, Liew AW.

BMC Bioinformatics. 2008 Apr 23;9:210. doi: 10.1186/1471-2105-9-210.

19.

Exact and heuristic algorithms for weighted cluster editing.

Rahmann S, Wittkop T, Baumbach J, Martin M, Truss A, Böcker S.

Comput Syst Bioinformatics Conf. 2007;6:391-401.

20.

Metric for measuring the effectiveness of clustering of DNA microarray expression.

Loganantharaj R, Cheepala S, Clifford J.

BMC Bioinformatics. 2006 Sep 6;7 Suppl 2:S5.

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