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Complexity
Volume 2017 (2017), Article ID 5024867, 10 pages
https://doi.org/10.1155/2017/5024867
Research Article

FAACOSE: A Fast Adaptive Ant Colony Optimization Algorithm for Detecting SNP Epistasis

1Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Caoan Road 4800, Shanghai 201804, China
2Science Computing and Intelligent Information Processing of Guang Xi Higher Education Key Laboratory, Guangxi Teachers Education University, Nanning, Guangxi 530001, China

Correspondence should be addressed to De-Shuang Huang; nc.ude.ijgnot@gnauhsd

Received 31 March 2017; Accepted 24 July 2017; Published 7 September 2017

Academic Editor: Jianxin Wang

Copyright © 2017 Lin Yuan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The epistasis is prevalent in the SNP interactions. Some of the existing methods are focused on constructing models for two SNPs. Other methods only find the SNPs in consideration of one-objective function. In this paper, we present a unified fast framework integrating adaptive ant colony optimization algorithm with multiobjective functions for detecting SNP epistasis in GWAS datasets. We compared our method with other existing methods using synthetic datasets and applied the proposed method to Late-Onset Alzheimer’s Disease dataset. Our experimental results show that the proposed method outperforms other methods in epistasis detection, and the result of real dataset contributes to the research of mechanism underlying the disease.