mrmr-abc: a hybrid gene selection algorithm for cancer classification using microarray gene expression profiling

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ID: 162097
2015
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Abstract
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.
Reference Key
alshamlan2015biomedmrmr-abc: Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Hala Alshamlan;Ghada Badr;Yousef Alohali
Journal spectrochimica acta - part a: molecular and biomolecular spectroscopy
Year 2015
DOI
10.1155/2015/604910
URL
http://dx.doi.org/10.1155/2015/604910
Keywords
Medicine

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