2014 | OriginalPaper | Buchkapitel
A Novel Two-Stage Multi-objective Ant Colony Optimization Approach for Epistasis Learning
verfasst von : Peng-Jie Jing, Hong-Bin Shen
Erschienen in: Pattern Recognition
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Recently, genome-wide association study (GWAS) which aims to discover genetic effects in phenotypic traits is a hot issue in genetic epidemiology. Epistasis known as genetic interaction is an important challenge in GWAS since it explains most individual susceptibility to complex diseases and it is difficult to detect due to its non-linearity. Here we present a novel two-stage method based on multi-objective ant colony optimization for epistasis learning. We conduct a lot of experiments on a wide range of simulated datasets and compare the outcome of our method with some other recent epistasis learning methods like AntEpiSeeker, Bayesian epistasis association mapping (BEAM) and BOolean Operation-based Screening and Testing (BOOST) method, finding that our method has a high power and is time efficient to learn epistatic interactions. We also do experiments in the real Late-onset Alzheimer’s disease (LOAD) dataset and the results substantiate that our method has a potential in searching the suspicious epistasis in large scale real GWAS datasets.