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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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Published in:BioMed Research International pp. 1 - 12
Main Authors: Yin, Ying, Guan, Boxin, Zhao, Yuhai, Li, Yuan
Format: algorithm equations & formulas research tables/charts Journal Article
Published: Wiley-Blackwell 8/25/2020
Online Access:View this record in EBSCOhost
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      dt: 8/25/2020
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      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        145298110
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        10.1155/2020/5610658
        145298110
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        atl: SAMA: A Fast Self-Adaptive Memetic Algorithm for Detecting SNP-SNP Interactions Associated with Disease.
      aug:
        au:
          Yin, Ying
          Guan, Boxin
          Zhao, Yuhai
          Li, Yuan
        affil: Key Laboratory of Intelligent Computing in Medical Image, Minister of Education, and School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
      sug:
        subj:
          Polymorphism, Single Nucleotide Physiology
          Genetic Screening Methods
          Mutation
          Genome Wide Association Study
      ab: 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.
      pubtype: Academic Journal
      doctype:
        algorithm
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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