蛋白质相互作用网络分析的图聚类方法研究进展

随着可获得的大规模蛋白质相互作用数据的迅速增长,从系统水平上对细胞机制的基本组件和结构的理解成为了一种可能。如今所面临的最大挑战是如何通过分析此类复杂的相互作用数据来反映细胞组织、进程以及功能的规律。基于图理论的聚类方法是分析蛋白质相互作用数据的有效手段。本文将从蛋白质相互作用网络(PPI网络)的图模型、聚类算法、评估方法及应用几个方面描述PPI网络聚类分析的最新研究进展。最后,讨论该方向研究所面临的挑战及进一步的研究方向。

Progress on Graph-Based Clustering Methods for the Analysis of Protein-Protein Interaction Networks

With the increase of large-scale protein-protein interaction data available, it has been possible to understand the basic components and organization of the cell mechanism from the system level. The challenge is how to analyze such complex interacting data to reveal the principles of cellular organization, processes and functions. Many studies have shown that using graph-based clustering methods is an effective approach to analyzing protein-protein interaction data. In this review, several aspects will be presented to describe the recent advances in clustering methods for protein interaction networks, such as the graph models of the PPI network, clustering methods, evaluation methods and applications. Finally, the challenges and directions for future research will be discussed.