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Literature DB >> 12801868

Coupled two-way clustering analysis of breast cancer and colon cancer gene expression data.

Gad Getz¹, Hilah Gal, Itai Kela, Daniel A Notterman, Eytan Domany.

Abstract

UNLABELLED: We present and review coupled two-way clustering, a method designed to mine gene expression data. The method identifies submatrices of the total expression matrix, whose clustering analysis reveals partitions of samples (and genes) into biologically relevant classes. We demonstrate, on data from colon and breast cancer, that we are able to identify partitions that elude standard clustering analysis. AVAILABILITY: Free, at http://ctwc.weizmann.ac.il.. SUPPLEMENTARY INFORMATION: http://www.weizmann.ac.il/physics/complex/compphys/bioinfo2/

Entities: Chemical Disease

Mesh：

Year: 2003 PMID： 12801868 DOI： 10.1093/bioinformatics/btf876

Source DB: PubMed Journal: Bioinformatics ISSN： 1367-4803 Impact factor: 6.937

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Cited

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Coupled two-way clustering analysis of breast cancer and colon cancer gene expression data.

1. Super paramagnetic clustering of DNA sequences.

2. Quantitative arbor analytics: unsupervised harmonic co-clustering of populations of brain cell arbors based on L-measure.

3. Feature Import Vector Machine: A General Classifier with Flexible Feature Selection.

4. Correlation signature of the macroscopic states of the gene regulatory network in cancer.

5. Expression profiles of Natural Killer Group 2D Ligands (NGK2DLs) in colorectal carcinoma and changes in response to chemotherapeutic agents.

6. ModuleFinder and CoReg: alternative tools for linking gene expression modules with promoter sequences motifs to uncover gene regulation mechanisms in plants.

7. Super paramagnetic clustering of protein sequences.

8. Correlated mutation analysis on the catalytic domains of serine/threonine protein kinases.

9. Unravelling the hidden heterogeneities of diffuse large B-cell lymphoma based on coupled two-way clustering.

10. Unraveling the hidden heterogeneities of breast cancer based on functional miRNA cluster.