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A Reliable Neighbor-Based Method for Identifying Essential Proteins by Integrating Gene Expressions, Orthology,and Subcellular Localization Information

A Reliable Neighbor-Based Method for Identifying Essential Proteins by Integrating Gene Expressions, Orthology,and Subcellular Localization Information

作     者:Min Li Zhibei Niu Xiaopei Chen Ping Zhong Fangxiang Wu Yi Pan 

作者机构:the School of Information Science and EngineeringCentral South University the Department of Mechanical Engineering and Division of Biomedical EngineeringUniversity of Saskatchewan the Department of Computer Science Georgia State University 

出 版 物:《Tsinghua Science and Technology》 (清华大学学报(自然科学版(英文版))

年 卷 期:2016年第21卷第6期

页      面:668-677页

核心收录:

学科分类:0710[理学-生物学] 071010[理学-生物化学与分子生物学] 081704[工学-应用化学] 07[理学] 08[工学] 0817[工学-化学工程与技术] 

基  金:supported by the National Natural Science Foundation for Excellent Young Scholars(No.61622213) the National Natural Science Foundation of China(Nos.61232001,61370024,and 61428209) 

主  题:essential protein reliable neighbors GOS orthology subcellular localization information 

摘      要:Essential proteins are those necessary for the survival or reproduction of species and discovering such essential proteins is fundamental for understanding the minimal requirements for cellular life, which is also meaningful to the disease study and drug design. With the development of high-throughput techniques, a large number of Protein-Protein Interactions(PPIs) can be used to identify essential proteins at the network level. Up to now, though a series of network-based computational methods have been proposed, it is still a challenge to improve the prediction precision as the high false positives in PPI networks. In this paper, we propose a new method GOS to identify essential proteins by integrating the Gene expressions, Orthology, and Subcellular localization *** gene expressions and subcellular localization information are used to determine whether a neighbor in the PPI network is reliable. Only reliable neighbors are considered when we analyze the topological characteristics of a protein in a PPI network. We also analyze the orthologous attributes of each protein to reflect its conservative features, and use a random walk model to integrate a protein's topological characteristics and its orthology. The experimental results on the yeast PPI network show that the proposed method GOS outperforms the ten existing methods DC, BC, CC, SC, EC, IC, NC, Pe C, ION, and CSC.

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