mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling

• Published in: BioMed research international Vol. 2015; no. 2015; pp. 1 - 15
• Main Authors: Alshamlan, Hala, Alohali, Yousef, Badr, Ghada
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2015; John Wiley & Sons, Inc; Hindawi Limited
• Subjects: Algorithms; Biomedical research; Cancer; Classification; Diagnosis; Gene expression; Gene Expression Profiling; Gene Expression Regulation, Neoplastic - genetics; Genes; Humans; Microarray Analysis - methods; Neoplasms - classification; Neoplasms - genetics; Observations; Optimization; Support Vector Machine; Support vector machines; Swarm intelligence
• ISSN: 2314-6133; 2314-6141
• DOI: 10.1155/2015/604910

An artificial bee colony (ABC) is a relatively recent swarm intelligence optimization approach. In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile. In addition, we propose an innovative feature selection algorithm, minimum redundancy maximum relevance (mRMR), and combine it with an ABC algorithm, mRMR-ABC, to select informative genes from microarray profile. The new approach is based on a support vector machine (SVM) algorithm to measure the classification accuracy for selected genes. We evaluate the performance of the proposed mRMR-ABC algorithm by conducting extensive experiments on six binary and multiclass gene expression microarray datasets. Furthermore, we compare our proposed mRMR-ABC algorithm with previously known techniques. We reimplemented two of these techniques for the sake of a fair comparison using the same parameters. These two techniques are mRMR when combined with a genetic algorithm (mRMR-GA) and mRMR when combined with a particle swarm optimization algorithm (mRMR-PSO). The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods. This shows that mRMR-ABC is a promising approach for solving gene selection and cancer classification problems.