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IOBLPSO PSOMiner

IOBLPSO:

Nonlinear interactions among single nucleotide polymorphisms (SNPs), or SNP-SNP interactions, have been receiving increasing attentions 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, with mutual information as its fitness function, for the detection of SNP-SNP interactions. Highlights of IOBLPSO are the introduction of three strategies, namely, opposition-based learning (OBL), dynamic inertia weight, and a post-procedure. Among them, OBL is the core, which is presented in the stage of updating particle experiences and common knowledge of swarm, not only to enhance the global explorative ability, but also to avoid premature convergence. Dynamic inertia weight is computed before the stage of updating particle velocities to allow particles to cover a wider search space when the considered SNP is likely to be a random one, and to converge on promising regions of the search space while capturing a highly suspected SNP. The post-procedure is used as the final stage to carry out a deep search in highly suspected SNP sets. Experiments of IOBLPSO are performed on lots of simulation data sets under the evaluation measures of both detection power and computational complexity. Results demonstrate that IOBLPSO is promising in detecting all simulation models of SNP-SNP interactions. IOBLPSO is also applied on data set of age-related macular degeneration (AMD). Results show the strength of IOBLPSO on real applications, and capture important features of genetic architecture of AMD that have not been described previously, providing new clues for biologists on the exploration of AMD associated SNPs. IOBLPSO might be an alternative to existing methods for detecting SNP-SNP interactions.
 

PSOMiner:

Most of complex diseases are believed to be mainly caused by epistatic interactions of pair single nucleotide polymorphisms (SNPs), namely, SNP-SNP interactions. Though many works have been done for the detection of SNP-SNP interactions, the algorithmic development is still ongoing due to their mathematical and computational complexities. In this study, we proposed a method, PSOMiner, based on the generalized particle swarm optimization algorithm, with mutual information as its fitness function, for the detection of SNP-SNP interaction that has the highest pathogenic effect in a SNP data set. Experiments of PSOMiner are performed on six simulation data sets under the criteria of detection power. Results demonstrate that PSOMiner is promising for the detection of SNP-SNP interaction. In addition, the application of PSOMiner on a real age-related macular degeneration (AMD) data set provides several new clues for the exploration of AMD associated SNPs that have not been described previously. PSOMiner might be an alternative to existing methods for detecting SNP-SNP interactions.
 

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