Key Laboratory of Intelligent Computing in Medical Image, Minister of Education, and School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China.
School of Information Science and Technology, North China University of Technology, Beijing 100144, China.
Biomed Res Int. 2020 Aug 24;2020:5610658. doi: 10.1155/2020/5610658. eCollection 2020.
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.
检测与疾病相关的 SNP-SNP 相互作用在全基因组关联研究 (GWAS) 中具有重要意义。由于计算负担大且疾病模型多样，现有方法在检测能力低和运行时间长方面存在缺陷。针对这些缺陷，本文提出了一种快速自适应遗传算法 (SAMA)。在该方法中，改进了标准遗传算法的交叉、变异和选择，使 SAMA 能够适应与疾病相关的 SNP-SNP 相互作用的检测。此外，引入了一种自适应局部搜索算法来提高所提出方法的检测能力。在各种模拟数据集和真实生物数据集上对 SAMA 进行了评估，并与最近基于进化算法开发的其他四种方法 (FHSA-SED、AntEpiSeeker、IEACO 和 DESeeker) 进行了比较研究。广泛实验的结果表明，SAMA 在检测能力和运行时间方面优于其他四种比较方法。
