Essential Protein Prediction Based on Shuffled Frog-Leaping Algorithm
Essential Protein Prediction Based on Shuffled Frog-Leaping Algorithm作者机构：School of Computer Science Shaanxi Normal University
出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))
年 卷 期：2021年第30卷第4期
页 面：704-711页
核心收录：
学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0711[理学-系统科学] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported by the National Natural Science Foundation of China (No.61972451, No.61672334, No.61902230) the Fundamental Research Funds for the Central Universities,Shaanxi Normal University (GK201901010)
主 题：Computational biology Essential protein Protein-protein interaction(PPI) network Shuffled frog leap algorithm
摘 要：Essential proteins are integral parts of living organisms. The prediction of essential proteins facilitates to discover disease genes and drug targets. The prediction precision and robustness of most of existing identification methods are not satisfactory. In this paper,we propose a novel essential proteins prediction method(EPSFLA), which applies Shuffled frog-leaping algorithm(SFLA), and integrates several biological information with network topological structure to identify essential ***, the topological property and several biological properties(function annotation, subcellular localization,protein complex, and orthology) are integrated and utilized to weight protein-protein interaction *** the position of a frog is encoded and denotes a candidate essential protein set. The frog population continuously evolve by means of local exploration and global exploration until termination criteria for algorithm are satisfied. Finally, those proteins contained in the best frog are regarded as predicted essential proteins. The experimental results show that EPSFLA outperforms some well-known prediction methods in terms of various *** proposed method aims to provide a new perspective for essential protein prediction.
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