ArticleBioMed research international2015
An Improved Opposition-Based Learning Particle Swarm Optimization for the Detection of SNP-SNP Interactions.
Article in BioMed research international, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- An efficient multilevel image thresholding method based on improved heap-based optimizer.Scientific reports · 2023Article
- An improved harris hawks optimization algorithm based on chaotic sequence and opposite elite learning mechanism.PloS one · 2023Article
- Multi-Objective Artificial Bee Colony Algorithm Based on Scale-Free Network for Epistasis Detection.Genes · 2022Article
- A Secure High-Order Gene Interaction Detecting Method for Infectious Diseases.Computational and mathematical methods in medicine · 2022Article
- Epi-GTBN: an approach of epistasis mining based on genetic Tabu algorithm and Bayesian network.BMC bioinformatics · 2019Article
- LBMC bioinformatics · 2019Article
- The computational prediction of drug-disease interactions using the dual-network LBMC bioinformatics · 2019Article
- FDHE-IW: A Fast Approach for Detecting High-Order Epistasis in Genome-Wide Case-Control Studies.Genes · 2018Article
- Niche harmony search algorithm for detecting complex disease associated high-order SNP combinations.Scientific reports · 2017Article
- FHSA-SED: Two-Locus Model Detection for Genome-Wide Association Study with Harmony Search Algorithm.PloS one · 2016Article
- Detecting Susceptibility to Breast Cancer with SNP-SNP Interaction Using BPSOHS and Emotional Neural Networks.BioMed research international · 2016Article
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6 authors.
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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 converges on promising regions of the search space while capturing a highly suspected SNP. The postprocedure is used to carry out a deep search in highly suspected SNP sets. Experiments of IOBLPSO are performed on both simulation data sets and a real data set of age-related macular degeneration, results of which demonstrate that IOBLPSO is promising in detecting SNP-SNP interactions. IOBLPSO might be an alternative to existing methods for detecting SNP-SNP interactions.
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