An Improved Opposition-Based Learning Particle Swarm Optimization for the Detection of SNP-SNP Interactions.

ABSTRACT: SNP-SNP interactions have been receiving increasing attention in understanding the mechanism underlying susceptibility to complex diseases. Though many works have been done for the detection of SNP-SNP interactions, the algorithmic development is still ongoing. In this study, an improved opposition-based learning particle swarm optimization (IOBLPSO) is proposed for the detection of SNP-SNP interactions. Highlights of IOBLPSO are the introduction of three strategies, namely, opposition-based learning, dynamic inertia weight, and a postprocedure. Opposition-based learning not only enhances the global explorative ability, but also avoids premature convergence. Dynamic inertia weight allows particles to cover a wider search space when the considered SNP is likely to be a random one and conver

SUBMITTER: Shang J 

PROVIDER: S-EPMC4509494 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature