Mining expressed sequence tags identifies cancer markers of clinical interest.

ABSTRACT:

Background

Gene expression data are a rich source of information about the transcriptional dis-regulation of genes in cancer. Genes that display differential regulation in cancer are a subtype of cancer biomarkers.

Results

We present an approach to mine expressed sequence tags to discover cancer biomarkers. A false discovery rate analysis suggests that the approach generates less than 22% false discoveries when applied to combined human and mouse whole genome screens. With this approach, we identify the 200 genes most consistently differentially expressed in cancer (called HM200) and proceed to characterize these genes. When used for prediction in a variety of cancer classification tasks (in 24 independent cancer microarray datasets, 59 classifications total), we show that H

SUBMITTER: Campagne F 

PROVIDER: S-EPMC1635568 | biostudies-literature | 2006 Nov

REPOSITORIES: biostudies-literature