EpiMOGA: An Epistasis Detection Method Based on a Multi-Objective Genetic Algorithm.

ABSTRACT: In genome-wide association studies, detecting high-order epistasis is important for analyzing the occurrence of complex human diseases and explaining missing heritability. However, there are various challenges in the actual high-order epistasis detection process due to the large amount of data, "small sample size problem", diversity of disease models, etc. This paper proposes a multi-objective genetic algorithm (EpiMOGA) for single nucleotide polymorphism (SNP) epistasis detection. The K2 score based on the Bayesian network criterion and the Gini index of the diversity of the binary classification problem were used to guide the search process of the genetic algorithm. Experiments were performed on 26 simulated datasets of different models and a real Alzheimer's disease dataset. The results

SUBMITTER: Chen Y 

PROVIDER: S-EPMC7911965 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature