SAMA: A Fast Self-Adaptive Memetic Algorithm for Detecting SNP-SNP Interactions Associated with Disease

Detecting SNP-SNP interactions associated with disease is significant in genome-wide association study (GWAS). Owing to intensive computational burden and diversity of disease models, existing methods have drawbacks on low detection power and long running time. To tackle these drawbacks, a fast self...

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Vydáno v:BioMed research international Ročník 2020; číslo 2020; s. 1 - 11
Hlavní autoři: Li, Yuan, Zhao, Yuhai, Guan, Boxin, Yin, Ying
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cairo, Egypt Hindawi Publishing Corporation 2020
Hindawi
John Wiley & Sons, Inc
Témata:
Adaptive algorithms
Adaptive search techniques
Algorithms
Comparative studies
Computer applications
Computer Simulation
Datasets
Disease
Epistasis, Genetic - genetics
Evolutionary algorithms
Genetic algorithms
Genome-wide association studies
Genome-Wide Association Study - methods
Genomes
Genotype & phenotype
Humans
Mutation
Optimization algorithms
Polymorphism, Single Nucleotide - genetics
Search algorithms
ISSN:2314-6133, 2314-6141, 2314-6141
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Popis
Shrnutí:Detecting SNP-SNP interactions associated with disease is significant in genome-wide association study (GWAS). Owing to intensive computational burden and diversity of disease models, existing methods have drawbacks on low detection power and long running time. To tackle these drawbacks, a fast self-adaptive memetic algorithm (SAMA) is proposed in this paper. In this method, the crossover, mutation, and selection of standard memetic algorithm are improved to make SAMA adapt to the detection of SNP-SNP interactions associated with disease. Furthermore, a self-adaptive local search algorithm is introduced to enhance the detecting power of the proposed method. SAMA is evaluated on a variety of simulated datasets and a real-world biological dataset, and a comparative study between it and the other four methods (FHSA-SED, AntEpiSeeker, IEACO, and DESeeker) that have been developed recently based on evolutionary algorithms is performed. The results of extensive experiments show that SAMA outperforms the other four compared methods in terms of detection power and running time.
Bibliografie:ObjectType-Article-1
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Guest Editor: Quan Zou
ISSN:2314-6133
2314-6141
2314-6141
DOI:10.1155/2020/5610658