Prediction potential of candidate biomarker sets identified and validated on gene expression data from multiple datasets.

ABSTRACT:

Background

Independently derived expression profiles of the same biological condition often have few genes in common. In this study, we created populations of expression profiles from publicly available microarray datasets of cancer (breast, lymphoma and renal) samples linked to clinical information with an iterative machine learning algorithm. ROC curves were used to assess the prediction error of each profile for classification. We compared the prediction error of profiles correlated with molecular phenotype against profiles correlated with relapse-free status. Prediction error of profiles identified with supervised univariate feature selection algorithms were compared to profiles selected randomly from a) all genes on the microarray platform and b) a list of known disease-relate

SUBMITTER: Gormley M 

PROVIDER: S-EPMC2211325 | biostudies-literature | 2007 Oct

REPOSITORIES: biostudies-literature