Gene selection for cancer classification by combining minimum redundancy maximum relevancy and bat-inspired algorithm. (2017)

• Record Type: Journal Article
• Authors: Alomari, Osama Ahmad; Khader, Ahamad Tajudin; Al-Betar, Mohammed Azmi; Abualigah, Laith Mohammad
• Is Part Of: International journal of data mining and bioinformatics. Volume 19:Number 1(2017)
• Page Start: 32
• Page End: 51
• Publication Date: 2017
• Subjects: bat-inspired algorithm -- optimisation -- gene selection -- MRMR -- SVM -- classification -- computational biology -- data mining -- gene expression -- DNA microarrays; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

Abstract

In this paper, the bat-inspired algorithm (BA) is tolerated to gene selection for cancer classification using microarray datasets. Microarray data consists of irrelevant, redundant, and noisy genes. Gene selection problem is tackled by determining the most informative genes taken from microarray data to accurately diagnose the cancer disease. Gene selection problem is widely solved by optimisation algorithms. BA is a recent swarm-based algorithm, which imitates the echolocation system of bat individuals. It has been successfully applied to several optimisation problems. Gene selection is tackled by combining two stages, namely, filter stage, which uses Minimum Redundancy Maximum Relevancy (MRMR) method; and wrapper stage, which uses BA and SVM. To test the accuracy performance of the proposed method, ten microarray datasets were used. For comparative evaluation, the proposed method was compared with popular gene selection methods. The proposed method achieves comparable results of some datasets and produced new results for one dataset.