基于神经网络和粒子群算法的遗传位点与患病信息的关联性分析  

Correlation analysis of genetic site and disease information based on neural networks and particle swarm optimization

作　　者：李杰[1] 李志强[2] 刘晓[1] 闫白鹭 

机构地区：[1]北京化工大学经济管理学院,北京100029 [2]北京化工大学理学院,北京100029

出　　处：《北京化工大学学报（自然科学版）》2018年第1期97-102,共6页Journal of Beijing University of Chemical Technology(Natural Science Edition)

基　　金：北京化工大学研究生教改项目(11120024018)

摘　　要：基于遗传疾病与某些遗传基因位点存在的较强关联性,并考虑到位点间存在交互作用的情形,提出了关联性最强的位点组合的筛选方法。将每个候选位点组合对应的基于神经网络的预报准确率作为评价标准,用粒子群算法(PSO)通过迭代逼近找出最优的位点组合,并与神经网络权重分析法进行比较。结果表明,由本文方法得到的位点组合预报精度更高,对患病情况有着较好的识别效果,可为遗传疾病诊断等提供参考方法。The method of screening the most powerful loci combinations has been studied under consideration of the interactions between loci when genetic diseases are associated with these genetic loci. In this paper, the prediction accuracy based on neural networks is taken as the evaluation criterion to find the optimal combination of loci by the particle swarm algorithm through iterative approximation. Compared with the weight analysis method, this method has higher accuracy, and has a good recognition effect for a disease, and can thus provide a reference for disease diagnosis.

关 键 词：遗传位点 交互作用 粒子群算法(PSO) 神经网络 

分 类 号：F064.1[经济管理—政治经济学]