EpiACO - a method for identifying epistasis based on ant Colony optimization algorithm.

ABSTRACT:

Background

Identifying epistasis or epistatic interactions, which refer to nonlinear interaction effects of single nucleotide polymorphisms (SNPs), is essential to understand disease susceptibility and to detect genetic architectures underlying complex diseases. Though many works have been done for identifying epistatic interactions, due to their methodological and computational challenges, the algorithmic development is still ongoing.

Results

In this study, a method epiACO is proposed to identify epistatic interactions, which based on ant colony optimization algorithm. Highlights of epiACO are the introduced fitness function Svalue, path selection strategies, and a memory based strategy. The Svalue leverages the advantages of both mutual information and Bayesi

SUBMITTER: Sun Y 

PROVIDER: S-EPMC5500974 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature