A step-by-step classification algorithm of protein secondary structures based on double-layer SVM model

作     者：Ge, Yongzhen Zhao, Shuo Zhao, Xiqiang 

作者机构：Ocean Univ China Sch Math Sci Qingdao 266100 Peoples R China Ocean Univ China Coll Informat Sci & Engn Qingdao 266100 Peoples R China 

出 版 物：《GENOMICS》 (基因组学)

年 卷 期：2020年第112卷第2期

页      面：1941-1946页

核心收录：

学科分类：0710[理学-生物学] 07[理学] 09[农学] 0836[工学-生物工程] 

基　　金：National Natural Science Foundation of China 

主　　题：Step-by-step classification algorithm Double-layer SVM Protein structural class prediction Secondary structure 

摘      要：In this paper, a step-by-step classification algorithm based on double-layer SVM model is constructed to predict the secondary structure of proteins. The most important feature of this algorithm is to improve the prediction accuracy of alpha+beta and alpha/beta classes through transforming the prediction of two classes of proteins, alpha+beta and alpha/beta classes, with low accuracy in the past, into the prediction of all-alpha and all-beta classes with high accuracy. A widely-used dataset, 25PDB dataset with sequence similarity lower than 40%, is used to evaluate this method. The results show that this method has good performance, and on the basis of ensuring the accuracy of other three structural classes of proteins, the accuracy of alpha+beta class proteins is improved significantly.