计算机科学
瓶颈
水准点(测量)
聚类分析
领域(数学)
鉴定(生物学)
蛋白质-蛋白质相互作用
节点(物理)
计算生物学
人工智能
数据挖掘
机器学习
化学
生物
数学
结构工程
植物
生物化学
工程类
嵌入式系统
纯数学
地理
大地测量学
作者
Zhourun Wu,Qing Liao,Bin Liu
摘要
Abstract Protein complexes are the fundamental units for many cellular processes. Identifying protein complexes accurately is critical for understanding the functions and organizations of cells. With the increment of genome-scale protein–protein interaction (PPI) data for different species, various computational methods focus on identifying protein complexes from PPI networks. In this article, we give a comprehensive and updated review on the state-of-the-art computational methods in the field of protein complex identification, especially focusing on the newly developed approaches. The computational methods are organized into three categories, including cluster-quality-based methods, node-affinity-based methods and ensemble clustering methods. Furthermore, the advantages and disadvantages of different methods are discussed, and then, the performance of 17 state-of-the-art methods is evaluated on two widely used benchmark data sets. Finally, the bottleneck problems and their potential solutions in this important field are discussed.
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