School of Computer Science and Engineering, Northeastern University, Shenyang 110819, PR China.
School of Computer Science and Engineering, Northeastern University, Shenyang 110819, PR China.
Comput Biol Chem. 2018 Dec;77:354-362. doi: 10.1016/j.compbiolchem.2018.11.001. Epub 2018 Nov 12.
Single Nucleotide polymorphisms (SNPs) are usually used as biomarkers for research and analysis of genome-wide association study (GWAS). Moreover, the epistatic interaction of SNPs is an important factor in determining the susceptibility of individuals to complex diseases. Nowadays, the detection of epistatic interactions not only attracts attention of many researchers but also brings new challenges. It is of great significance to mine epistatic interactions from large-scale data for the combinatorial explosion problem of loci. Hence, it is necessary to improve an efficient algorithm for solving the problem. In this article, a novel ant colony optimization based on automatic adjustment mechanism (AA-ACO) is proposed. The mechanism automatically adjusts the behaviour of artificial ants according to the real-time feedback information so that the algorithm can run at its best. This study also compares AA-ACO with ACO, AntEpiSeeker, AntMiner, MACOED and epiACO in a set of simulated data sets and a real genome-wide data. As shown by the experimental results, the proposed algorithm is superior to the other algorithms.
单核苷酸多态性（SNPs）通常被用作全基因组关联研究（GWAS）中研究和分析的生物标志物。此外，SNP 的上位性相互作用是决定个体对复杂疾病易感性的一个重要因素。如今，SNP 上位性相互作用的检测不仅引起了许多研究人员的关注，也带来了新的挑战。对于由于遗传位点的组合爆炸而导致的大规模数据，挖掘 SNP 上位性相互作用具有重要意义。因此，有必要改进一种有效的算法来解决这个问题。在本文中，提出了一种基于自动调整机制的新型蚁群优化算法（AA-ACO）。该机制根据实时反馈信息自动调整人工蚂蚁的行为，使算法能够达到最佳运行状态。本研究还在一组模拟数据集和一个真实的全基因组数据上，将 AA-ACO 与 ACO、AntEpiSeeker、AntMiner、MACOED 和 epiACO 进行了比较。实验结果表明，所提出的算法优于其他算法。
