FAACOSE swMATH ID: 34291 Software Authors: Yuan, Lin; Yuan, Chang-An; Huang, De-Shuang Description: FAACOSE: a fast adaptive ant colony optimization algorithm for detecting SNP epistasis. 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. Homepage: https://www.hindawi.com/journals/complexity/2017/5024867/ Keywords: fast adaptive ant colony optimization algorithm Related Software: SFAPS; chifish; MACOED; AntEpiSeeker; eQTL epistasis; IMPUTE Cited in: 1 Publication Standard Articles 1 Publication describing the Software, including 1 Publication in zbMATH Year FAACOSE: a fast adaptive ant colony optimization algorithm for detecting SNP epistasis. Zbl 1373.93185Yuan, Lin; Yuan, Chang-An; Huang, De-Shuang 2017 Cited by 3 Authors 1 Huang, Deshuang 1 Yuan, Changan 1 Yuan, Lin Cited in 1 Serial 1 Complexity Cited in 3 Fields 1 Operations research, mathematical programming (90-XX) 1 Biology and other natural sciences (92-XX) 1 Systems theory; control (93-XX) Citations by Year