MACOED: a multi-objective ant colony optimization algorithm for SNP epistasis detection in genome-wide association studies
Author(s) -
Peng-Jie Jing,
HongBin Shen
Publication year - 2014
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btu702
Subject(s) - computer science , ant colony optimization algorithms , genetic algorithm , data mining , epistasis , heuristic , bayesian probability , false positive rate , machine learning , algorithm , artificial intelligence , chemistry , biochemistry , gene
The existing methods for genetic-interaction detection in genome-wide association studies are designed from different paradigms, and their performances vary considerably for different disease models. One important reason for this variability is that their construction is based on a single-correlation model between SNPs and disease. Due to potential model preference and disease complexity, a single-objective method will therefore not work well in general, resulting in low power and a high false-positive rate.
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