New feature subset selection procedures for classification of expression profiles.

ABSTRACT:

Background

Methods for extracting useful information from the datasets produced by microarray experiments are at present of much interest. Here we present new methods for finding gene sets that are well suited for distinguishing experiment classes, such as healthy versus diseased tissues. Our methods are based on evaluating genes in pairs and evaluating how well a pair in combination distinguishes two experiment classes. We tested the ability of our pair-based methods to select gene sets that generalize the differences between experiment classes and compared the performance relative to two standard methods. To assess the ability to generalize class differences, we studied how well the gene sets we select are suited for learning a classifier.

Results

We show that the gene set

SUBMITTER: Bo T 

PROVIDER: S-EPMC115205 | biostudies-literature | 2002

REPOSITORIES: biostudies-literature