中心性
刀切重采样
钥匙(锁)
鉴定(生物学)
计算机科学
基因本体论
公制(单位)
稳健性(进化)
计算生物学
数据挖掘
人工智能
生物
数学
基因
遗传学
工程类
生态学
计算机安全
统计
组合数学
基因表达
估计员
运营管理
作者
Yan Han,Maolin Liu,Zhixiao Wang
出处
期刊:Mathematical Biosciences and Engineering
[American Institute of Mathematical Sciences]
日期:2023-01-01
卷期号:20 (10): 18191-18206
摘要
<abstract><p>Identifying key proteins based on protein-protein interaction networks has emerged as a prominent area of research in bioinformatics. However, current methods exhibit certain limitations, such as the omission of subcellular localization information and the disregard for the impact of topological structure noise on the reliability of key protein identification. Moreover, the influence of proteins outside a complex but interacting with proteins inside the complex on complex participation tends to be overlooked. Addressing these shortcomings, this paper presents a novel method for key protein identification that integrates protein complex information with multiple biological features. This approach offers a comprehensive evaluation of protein importance by considering subcellular localization centrality, topological centrality weighted by gene ontology (GO) similarity and complex participation centrality. Experimental results, including traditional statistical metrics, jackknife methodology metric and key protein overlap or difference, demonstrate that the proposed method not only achieves higher accuracy in identifying key proteins compared to nine classical methods but also exhibits robustness across diverse protein-protein interaction networks.</p></abstract>
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