NDLI: Detection of SNP-SNP interaction based on the generalized particle swarm optimization algorithm

Content Provider	IEEE Xplore Digital Library
Author	Changyi Ma Junliang Shang Shengjun Li Yan Sun
Copyright Year	2014
Description	Author affiliation: Sch. of Inf. Sci. & Eng., Qufu Normal Univ., Rizhao, China (Changyi Ma; Junliang Shang; Shengjun Li; Yan Sun)
Abstract	Most of complex diseases are believed to be mainly caused by epistatic interactions of pair single nucleotide poly-morphisms (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.
Starting Page	151
Ending Page	155
File Size	649609
Page Count	5
File Format	PDF
ISBN	9781479972944
ISSN	23250712
DOI	10.1109/ISB.2014.6990748
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	SNP-SNP Interaction Biological system modeling Mutual Information Genetics Data models Particle swarm optimization Bioinformatics Mutual information Particle Swarm Optimization Diseases
Content Type	Text
Resource Type	Article
Subject	Biochemistry, Genetics and Molecular Biology Modeling and Simulation Computer Science Applications

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