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Open Access Methodology article

Cancer characterization and feature set extraction by discriminative margin clustering

Kamesh Munagala1*, Robert Tibshirani2 and Patrick O Brown3

Author Affiliations

1 Department of Biochemistry, Stanford University School of Medicine. Address: 466 Gates Computer Science, Stanford CA 94305, USA

2 Department of Health Research and Policy, and Department of Statistics, HRP T101C, Stanford CA 94305, USA

3 Department of Biochemistry Stanford University School of Medicine, Beckman B439, Stanford CA 94305, USA

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BMC Bioinformatics 2004, 5:21  doi:10.1186/1471-2105-5-21

Published: 3 March 2004

Abstract

Background

A central challenge in the molecular diagnosis and treatment of cancer is to define a set of molecular features that, taken together, distinguish a given cancer, or type of cancer, from all normal cells and tissues.

Results

Discriminative margin clustering is a new technique for analyzing high dimensional quantitative datasets, specially applicable to gene expression data from microarray experiments related to cancer. The goal of the analysis is find highly specialized sub-types of a tumor type which are similar in having a small combination of genes which together provide a unique molecular portrait for distinguishing the sub-type from any normal cell or tissue. Detection of the products of these genes can then, in principle, provide a basis for detection and diagnosis of a cancer, and a therapy directed specifically at the distinguishing constellation of molecular features can, in principle, provide a way to eliminate the cancer cells, while minimizing toxicity to any normal cell.

Conclusions

The new methodology yields highly specialized tumor subtypes which are similar in terms of potential diagnostic markers.