Using Multi-Feature Weak Consensus Model to Discover Essential Proteins

作     者：Hu, Zhipeng Li, Gaoshi Luo, Xinlong Liu, Jiafei Wu, Jingli Peng, Wei Zhu, Xiaoshu 

作者机构：Guangxi Normal Univ Coll Comp Sci & Engn Key Lab Educ Blockchain & Intelligent Technol Minist EducGuangxi Key Lab Multisource Informat M Guilin 541004 Peoples R China Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming 650500 Peoples R China Guilin Univ Elect Sci & Technol Sch Comp & Informat Secur Sch Software Engn Guilin 541004 Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS》 

年 卷 期：2025年第22卷第1期

页      面：322-332页

核心收录：

基　　金：National Natural Science Foundation of China [62472202, 61972185, 62462020, 62141207, 62302107, 62366007] Guangxi Natural Science Foundation [2022GXNSFAA035625] Research Fund of Guangxi Key Lab of Multi-source Information Mining Security [24-A-03-01, 24-A-03-02, 20-A-01-03, 19-A-03-01] Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing, Innovation Project of Guangxi Graduate Education [YCSW2023180, YCSW2024222] 

主　　题：Proteins Computational biology Bioinformatics Feature extraction Biological system modeling Fuses Computational modeling Shape Reliability Minimax techniques Essential proteins neighborhood aggregation centrality protein-protein interaction weak consensus model 

摘      要：Essential proteins play an essential role in cell survival and replication. Currently, more and more computational methods are developed to identify essential proteins, which overcome the time-consuming, costly and inefficient shortcomings with biological experimental methods. In order to improve the recognition rate, some new methods by fusing multiple features are developed, but they seldom consider the connection among features. After analyzing a large number of methods based on multi-feature fusion, a phenomenon among features is found, called weak consensus, then a weak consensus model to fuse these features is proposed in this paper. After analyzing the relationship between a protein and its neighbors in protein-protein interaction networks, a new centrality, namely neighborhood aggregation centrality(NAC) is developed in this paper. Then, a Max-Min strategy is used to integrate NAC with Pearson correlation coefficient and Jaccard similarity coefficient based on gene expression data to obtain local importance score. In addition, orthologous feature score is used to measure proteins conservation. Finally, by using the weak consensus model to fuse orthologous feature score with local importance score, a new method WOL is proposed in this paper. Then experiments are performed on *** data. The results show that compared with WDC, PeC, ION, JDC, NCCO and E_POC, WOL has a higher recognition rate.