计算机应用研究2017，Vol.34Issue(5)：1346-1348,1378,4.DOI:10.3969/j.issn.1001-3695.2017.05.015
基于SAPSO-LSSVM的蛋白质模型质量评估
Predicting protein model quality assessment by SAPSO-LSSVM
摘要
Abstract
As the traditional methods evaluated the quality of models without considering the source information,this paper designed a new algorithm to access the quality of a protein structure based on least squares support vector machines.Firstly,considering the characteristics that simulated annealing (SA) algorithm could jump out of the local optimal solution and that particle swarm optimization (PSO) algorithm had a fast convergence speed,it developed a simulated annealing particle swarm optimization (SAPSO) algorithm;the next it optimized the parameters of LS-SVM that included C,γ by SAPSO algorithm;lastly the algorithm built an optimal model,which used to predict the quality score of a protein structure model.The experimental results show that the model of SAPSO algorithm optimized the parameters has less error in predicting protein structures and its prediction is more stable.关键词
蛋白质/模型质量/LS-SVM/模拟退火粒子群/参数优化Key words
protein/model quality/LS-SVM/simulated annealing particle swarm optimization(SAPSO)/optimize parameters分类
信息技术与安全科学引用本文复制引用
王鲜芳,张悦,王俊美..基于SAPSO-LSSVM的蛋白质模型质量评估[J].计算机应用研究,2017,34(5):1346-1348,1378,4.基金项目
国家自然科学基金资助项目(61173071) （61173071）
河南省高校创新人才支持计划项目(2012HASTIT011) （2012HASTIT011）
