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epiACO

With the rapidly development of DNA sequencing and bioinformatics technique, a huge amount of high-dimensional single-nucleotide polymorphism (SNP) data is genotyped. Although many methods for detection of the complex associations between SNPs and common diseases in genome-wide association studies have been proposed, some of them only confine to explore on single genetic markers. Recently, an increasing number of studies have conformed that one of the most important factors for emergence and development of complex diseases is the interactions between SNPs, that is to say, epistasis or epistatic interactions. In this study, a method named epiACO based on ant colony optimization algorithm is proposed for identifying epistatic interactions. In epiACO, a fitness function Svalue which combined mutual information with Bayesian networks is introduced for detecting epistatic interactions. Svalue has effectively solved the one-sided problem of one evaluation measure and can improve the detection power of epiACO. Another highlight is, a self-adaption adjustment parameter which is designed to improve the processing capacity of models that displaying no marginal effects. Unlike traditional process way, a memory based strategy is used to deal with the optimal solutions of epiACO. This memory based strategy can effectively improve the computational efficiency, enhance the processing ability of all optimal solutions and generate a more accurate way for detecting epistasis. Furthermore, a post-processing tactics is also employed to improve the power of detecting pure epistasis. Experiments of epiACO are compared with some other representative methods which are AntMiner, IACO, AntEpiSeeker and MACOED in both simulated and real age-related macular degeneration datasets. Results show that epiACO outperforms others in detection power for a large scale SNP datasets and might provides some significant clues on heuristics for inferring epistatic interactions.


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