Mining hub-based protein complexes in massive biological networks

Zhijie Lin, Yan Chen, Shiwei Wu, Yun Xiong, Yangyong Zhu, Guangyong Zheng
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Abstract

Advanced technologies are producing large-scale protein-protein interaction data at an ever increasing pace. Finding protein-protein interaction complexes from large PPI networks is a fundamental problem in bioinformatics. As a group of core proteins which interacts with other more proteins, hub proteins play a key role in protein complex and life activity. In this paper, we propose a novel topological model, HP*-complex, which defines the hub proteins of protein complex and extends to encompass the neighborhood of the hub proteins, for the initial structure of protein complexes. An algorithm based on the new topological model, called HPCMiner, is developed for identifying protein complexes from large PPI networks. The experiment results on real dataset show that our proposed algorithm detects many complexes having special biological significance. The results from a study on synthetic data sets demonstrate that the HPCMiner algorithm scales well with respect to data set size.
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在大规模生物网络中挖掘基于枢纽的蛋白质复合物
先进的技术正在以越来越快的速度产生大规模的蛋白质-蛋白质相互作用数据。从大型PPI网络中寻找蛋白质-蛋白质相互作用复合物是生物信息学中的一个基本问题。枢纽蛋白作为一组与其他蛋白质相互作用的核心蛋白,在蛋白质复合体和生命活动中起着关键作用。本文提出了一种新的拓扑模型HP*-complex,它定义了蛋白质复合体的枢纽蛋白,并扩展到枢纽蛋白的邻域,作为蛋白质复合体的初始结构。一种基于新拓扑模型的算法,称为HPCMiner,被开发用于从大型PPI网络中识别蛋白质复合物。在真实数据集上的实验结果表明,我们提出的算法能够检测到许多具有特殊生物学意义的复合物。对合成数据集的研究结果表明,HPCMiner算法在数据集大小方面具有良好的可伸缩性。
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