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Items: 1 to 20 of 209

1.

TimeClust: a clustering tool for gene expression time series.

Magni P, Ferrazzi F, Sacchi L, Bellazzi R.

Bioinformatics. 2008 Feb 1;24(3):430-2. Epub 2007 Dec 6.

2.

Divisive Correlation Clustering Algorithm (DCCA) for grouping of genes: detecting varying patterns in expression profiles.

Bhattacharya A, De RK.

Bioinformatics. 2008 Jun 1;24(11):1359-66. doi: 10.1093/bioinformatics/btn133. Epub 2008 Apr 10.

3.

ParaKMeans: Implementation of a parallelized K-means algorithm suitable for general laboratory use.

Kraj P, Sharma A, Garge N, Podolsky R, McIndoe RA.

BMC Bioinformatics. 2008 Apr 16;9:200. doi: 10.1186/1471-2105-9-200.

4.

Clustering of gene expression data: performance and similarity analysis.

Yin L, Huang CH, Ni J.

BMC Bioinformatics. 2006 Dec 12;7 Suppl 4:S19.

5.

An improved algorithm for clustering gene expression data.

Bandyopadhyay S, Mukhopadhyay A, Maulik U.

Bioinformatics. 2007 Nov 1;23(21):2859-65. Epub 2007 Aug 25.

6.

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.

7.
8.

How does gene expression clustering work?

D'haeseleer P.

Nat Biotechnol. 2005 Dec;23(12):1499-501. Review.

PMID:
16333293
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.

10.

STEM: a tool for the analysis of short time series gene expression data.

Ernst J, Bar-Joseph Z.

BMC Bioinformatics. 2006 Apr 5;7:191.

11.

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.

12.

Clustering of change patterns using Fourier coefficients.

Kim J, Kim H.

Bioinformatics. 2008 Jan 15;24(2):184-91. Epub 2007 Nov 19.

13.

An integrated tool for microarray data clustering and cluster validity assessment.

Bolshakova N, Azuaje F, Cunningham P.

Bioinformatics. 2005 Feb 15;21(4):451-5. Epub 2004 Dec 17.

14.

Hierarchical tree snipping: clustering guided by prior knowledge.

Dotan-Cohen D, Melkman AA, Kasif S.

Bioinformatics. 2007 Dec 15;23(24):3335-42. Epub 2007 Nov 7.

15.

Modeling and visualizing uncertainty in gene expression clusters using dirichlet process mixtures.

Rasmussen CE, de la Cruz BJ, Ghahramani Z, Wild DL.

IEEE/ACM Trans Comput Biol Bioinform. 2009 Oct-Dec;6(4):615-28. doi: 10.1109/TCBB.2007.70269.

PMID:
19875860
16.

Noise-robust soft clustering of gene expression time-course data.

Futschik ME, Carlisle B.

J Bioinform Comput Biol. 2005 Aug;3(4):965-88.

PMID:
16078370
17.

OrderedList--a bioconductor package for detecting similarity in ordered gene lists.

Lottaz C, Yang X, Scheid S, Spang R.

Bioinformatics. 2006 Sep 15;22(18):2315-6. Epub 2006 Jul 14.

18.

Combining sequence and time series expression data to learn transcriptional modules.

Kundaje A, Middendorf M, Gao F, Wiggins C, Leslie C.

IEEE/ACM Trans Comput Biol Bioinform. 2005 Jul-Sep;2(3):194-202.

PMID:
17044183
19.

VISDA: an open-source caBIG analytical tool for data clustering and beyond.

Wang J, Li H, Zhu Y, Yousef M, Nebozhyn M, Showe M, Showe L, Xuan J, Clarke R, Wang Y.

Bioinformatics. 2007 Aug 1;23(15):2024-7. Epub 2007 May 31.

20.

A knowledge-driven approach to cluster validity assessment.

Bolshakova N, Azuaje F, Cunningham P.

Bioinformatics. 2005 May 15;21(10):2546-7. Epub 2005 Feb 15.

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