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Open Access Open Badges 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



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.


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.


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