Cancer characterization and feature set extraction by discriminative margin clustering.
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.
Munagala
Kamesh
K
Department of Biochemistry, Stanford University School of Medicine, 466 Gates Computer Science, Stanford, CA 94305, USA. kamesh@cmgm.stanford.edu
Tibshirani
Robert
R
Brown
Patrick O
PO
D016000:Cluster Analysis; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D015972:Gene Expression Regulation, Neoplastic / Q000235:genetics; D052138:Genes, Neoplasm / Q000235:genetics; D006801:Humans; D025202:Molecular Diagnostic Techniques / Q000706:statistics & numerical data*; D009369:Neoplasms / Q000145:classification / Q000175:diagnosis* / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D011237:Predictive Value of Tests