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

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 redundan...

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Bibliographic Details
Published in:BioMed research international 2015-01, Vol.2015 (2015), p.1-15
Main Authors: Alshamlan, Hala, Alohali, Yousef, Badr, Ghada
Format: Article
Language:English
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
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Summary: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.
ISSN:2314-6133
2314-6141
DOI:10.1155/2015/604910