A feature selection method for classification within functional genomics experiments based on the proportional overlapping score.

ABSTRACT:

Background

Microarray technology, as well as other functional genomics experiments, allow simultaneous measurements of thousands of genes within each sample. Both the prediction accuracy and interpretability of a classifier could be enhanced by performing the classification based only on selected discriminative genes. We propose a statistical method for selecting genes based on overlapping analysis of expression data across classes. This method results in a novel measure, called proportional overlapping score (POS), of a feature's relevance to a classification task.

Results

We apply POS, along-with four widely used gene selection methods, to several benchmark gene expression datasets. The experimental results of classification error rates computed using the Random Forest, k

SUBMITTER: Mahmoud O 

PROVIDER: S-EPMC4141116 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature