Population-based incremental learning for the prediction of Homo sapiens’ protein secondary structure

作     者：Ye Chen Xiaoping Yuan Xiaohui Cang 

作者机构：School of Information and Control Engineering China University of Mining and Technology Xuzhou Jiangsu 221008 P. R. China Institute of Genetics Zhejiang University School of Medicine Hangzhou Zhejiang 310058 P. R. China 

出 版 物：《International Journal of Biomathematics》 (生物数学学报（英文版）)

年 卷 期：2019年第12卷第3期

页      面：1-21页

核心收录：

学科分类：0810[工学-信息与通信工程] 08[工学] 081002[工学-信号与信息处理] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：the National Natural Science Foundation of China (Grant No. 31400709 to X. C.) National Key Technology Support Program of China (Grant No. 2013BAK06B08) Scientific Research Fund of Zhejiang Provincial Education Department (China)(Grant No. Y201432207 to X. C.) Natural Science Fund of Jiangsu Province (China)(Grant No: BK20130187). 

主　　题：Population-based incremental learning Homo sapiens prediction of protein secondary structure 

摘      要：prediction of the protein secondary structure of Homo sapiens is one of the more important domains. Many methods have been used to feed forward neural networks or SVMs combined with a sliding window. This method’s mechanisms are too complex to be able to extract clear and straightforward physical meanings from it. This paper explores population-based incremental learning (PBIL), which is a method that combines the mechanisms of a generational genetic algorithm with simple competitive learning. The result shows that its accuracies are particularly associated with the Homo species. This new perspective reveals a number of different possibilities for the purposes of performance improvements.