A Novel Information Theoretic Approach to Gene Selection for Cancer Classification Using Microarray Data

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.

Cite this article as:

Naseem Imran, Togneri Roberto and Bennamoun Mohammed, A Novel Information Theoretic Approach to Gene Selection for Cancer Classification Using Microarray Data, Current Bioinformatics 2015; 10 (4) . https://dx.doi.org/10.2174/157489361004150922145751