Exploiting the Accumulated Evidence for Gene Selection in Microarray Gene Expression Data

TL;DR

This paper introduces a simple, cost-effective method for gene selection in microarray data that accumulates evidence across the search process, improving the quality of gene subsets for cancer classification.

Contribution

It proposes a novel evidence accumulation technique during gene subset search, enhancing feature selection effectiveness in microarray gene expression analysis.

Findings

Improved predictive accuracy of gene subsets.

Smaller gene sets with maintained or enhanced performance.

Method is simple and incurs negligible additional cost.

Abstract

Machine Learning methods have of late made significant efforts to solving multidisciplinary problems in the field of cancer classification using microarray gene expression data. Feature subset selection methods can play an important role in the modeling process, since these tasks are characterized by a large number of features and a few observations, making the modeling a non-trivial undertaking. In this particular scenario, it is extremely important to select genes by taking into account the possible interactions with other gene subsets. This paper shows that, by accumulating the evidence in favour (or against) each gene along the search process, the obtained gene subsets may constitute better solutions, either in terms of predictive accuracy or gene size, or in both. The proposed technique is extremely simple and applicable at a negligible overhead in cost.