z-logo
open-access-imgOpen Access
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.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom