A novel method incorporating gene ontology information for unsupervised clustering and feature selection.

ABSTRACT:

Background

Among the primary goals of microarray analysis is the identification of genes that could distinguish between different phenotypes (feature selection). Previous studies indicate that incorporating prior information of the genes' function could help identify physiologically relevant features. However, current methods that incorporate prior functional information do not provide a relative estimate of the effect of different genes on the biological processes of interest.

Results

Here, we present a method that integrates gene ontology (GO) information and expression data using Bayesian regression mixture models to perform unsupervised clustering of the samples and identify physiologically relevant discriminating features. As a model application, the method was applied

SUBMITTER: Srivastava S 

PROVIDER: S-EPMC2585795 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature