Title: Extracting a cancer model by enhanced ant colony optimisation algorithm

Authors: Reza Shamsaee; Mahmood Fathy; Ali Masoudi-Nejad

Addresses: High Performance Computation Laboratory (HPC lab), School of Computer Engineering, Iran University of Science and Technology, (IUST), Tehran, Iran; Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran ' High Performance Computation Laboratory (HPC lab), School of Computer Engineering, Iran University of Science and Technology, (IUST), Tehran, Iran ' Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran

Abstract: Although Ant-Miner has been used with relative ease for datasets with categorical data and small-sized feature vectors, microarray datasets, which contain a few samples with large amount of genes, are a totally different story. The Ant-Miner is an ant colony optimisation algorithm that extracts predictive rules from datasets and intrinsically works on discrete values. This study has developed a new algorithm, "Enhanced Ant-Miner" (EAM), based on previous works. EAM deals with continuous attributes as well as categorical ones and presents its captured models in the form of predictive rules. EAM has been tested versus SVM, CN2, K-means and hierarchical clustering and the results show that EAM is the best in the context of predictive accuracy. Additionally, its agent-based nature gives it a much more charming ability to speed up the whole process when compared to other trivial miners.

Keywords: data mining; abstract models; ant colony optimisation; enhanced ACO; microarrays; cancer modelling; agent-based systems; multi-agent systems; MAS; bioinformatics; prostate cancer; gliomas; sarcoma.

DOI: 10.1504/IJDMB.2014.062893

International Journal of Data Mining and Bioinformatics, 2014 Vol.10 No.1, pp.83 - 97

Received: 12 Dec 2011
Accepted: 26 Dec 2012

Published online: 21 Oct 2014 *

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