计算机科学
鉴定(生物学)
可靠性(半导体)
注释
虚假关系
计算生物学
网络拓扑
蛋白质功能预测
数据挖掘
交互网络
蛋白质-蛋白质相互作用
拓扑(电路)
机器学习
人工智能
基因
生物
遗传学
蛋白质功能
数学
计算机网络
物理
功率(物理)
组合数学
量子力学
植物
作者
Wei Zhang,Jian Xu,Yuanyuan Li,Xiufen Zou
标识
DOI:10.1142/s021972001950001x
摘要
The prediction of protein complexes based on the protein interaction network is a fundamental task for the understanding of cellular life as well as the mechanisms underlying complex disease. A great number of methods have been developed to predict protein complexes based on protein–protein interaction (PPI) networks in recent years. However, because the high throughput data obtained from experimental biotechnology are incomplete, and usually contain a large number of spurious interactions, most of the network-based protein complex identification methods are sensitive to the reliability of the PPI network. In this paper, we propose a new method, Identification of Protein Complex based on Refined Protein Interaction Network (IPC-RPIN), which integrates the topology, gene expression profiles and GO functional annotation information to predict protein complexes from the reconstructed networks. To demonstrate the performance of the IPC-RPIN method, we evaluated the IPC-RPIN on three PPI networks of Saccharomycescerevisiae and compared it with four state-of-the-art methods. The simulation results show that the IPC-RPIN achieved a better result than the other methods on most of the measurements and is able to discover small protein complexes which have traditionally been neglected.
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