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Gene Expression Analysis based on Ant Colony Optimisation Classification

Gene Expression Analysis based on Ant Colony Optimisation Classification

Gerald Schaefer
Copyright: © 2016 |Volume: 3 |Issue: 3 |Pages: 9
ISSN: 2334-4598|EISSN: 2334-4601|EISBN13: 9781466695986|DOI: 10.4018/IJRSDA.2016070104
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MLA

Schaefer, Gerald. "Gene Expression Analysis based on Ant Colony Optimisation Classification." IJRSDA vol.3, no.3 2016: pp.51-59. http://doi.org/10.4018/IJRSDA.2016070104

APA

Schaefer, G. (2016). Gene Expression Analysis based on Ant Colony Optimisation Classification. International Journal of Rough Sets and Data Analysis (IJRSDA), 3(3), 51-59. http://doi.org/10.4018/IJRSDA.2016070104

Chicago

Schaefer, Gerald. "Gene Expression Analysis based on Ant Colony Optimisation Classification," International Journal of Rough Sets and Data Analysis (IJRSDA) 3, no.3: 51-59. http://doi.org/10.4018/IJRSDA.2016070104

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Abstract

Microarray studies and gene expression analysis have received significant attention over the last few years and provide many promising avenues towards the understanding of fundamental questions in biology and medicine. In this paper, the authors investigate the application of ant colony optimisation (ACO) based classification for the analysis of gene expression data. They employ cAnt-Miner, a variation of the classical Ant-Miner classifier, which is capable of interpreting the numerical gene expression data. Experimental results on well-known gene expression datasets show that the ant-based approach is capable of extracting a compact rule base while providing good classification performance.

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