Informative Gene Selection for Microarray Classification via Adaptive Elastic Net with Conditional Mutual Information

TL;DR

This paper introduces AEN-CMI, an improved gene selection algorithm that combines adaptive elastic net with conditional mutual information, leading to better cancer classification with fewer genes.

Contribution

The paper proposes a novel AEN-CMI algorithm that enhances gene selection by integrating conditional mutual information into adaptive elastic net, improving classification accuracy and gene efficiency.

Findings

AEN-CMI outperforms other algorithms in classification accuracy.

AEN-CMI uses fewer genes to achieve better results.

Effective for colon cancer and leukemia gene screening.

Abstract

Due to the advantage of achieving a better performance under weak regularization, elastic net has attracted wide attention in statistics, machine learning, bioinformatics, and other fields. In particular, a variation of the elastic net, adaptive elastic net (AEN), integrates the adaptive grouping effect. In this paper, we aim to develop a new algorithm: Adaptive Elastic Net with Conditional Mutual Information (AEN-CMI) that further improves AEN by incorporating conditional mutual information into the gene selection process. We apply this new algorithm to screen significant genes for two kinds of cancers: colon cancer and leukemia. Compared with other algorithms including Support Vector Machine, Classic Elastic Net and Adaptive Elastic Net, the proposed algorithm, AEN-CMI, obtains the best classification performance using the least number of genes.