Title:A Novel Information Theoretic Approach to Gene Selection for Cancer Classification Using Microarray Data
Volume: 10
Issue: 4
Author(s): Imran Naseem, Roberto Togneri and Mohammed Bennamoun
Affiliation:
Keywords:
Gene selection, microarray data, tumor classification.
Abstract: In this research an efficient gene selection method called Discriminant Mutual Information
(DMI) algorithm is proposed. The DMI algorithm sequentially induces discrimination and relevance to
identify the most significant genes for tumor classification. In particular, in the first step the entire
gene population is decorrelated by the formation of gene-sets such that the genes with similar characteristics form a single
gene-set. The mutual information criterion is further employed to identify the most representative gene of each gene-set.
Extensive experiments have been conducted on six publicly available databases where the proposed DMI algorithm has
shown good results compared to a number of state-of-the-art approaches. Extensive computational analysis clearly reflects
the computational efficiency of the proposed approach, typically it requires only a few seconds for experimentation on
standard microarray datasets.
