School of Information Science and Engineering, Central South University, Changsha 410083, China.
BMC Genomics. 2010 Dec 1;11 Suppl 3(Suppl 3):S10. doi: 10.1186/1471-2164-11-S3-S10.
The increasing availability of large-scale protein-protein interaction data has made it possible to understand the basic components and organization of cell machinery from the network level. The arising challenge is how to analyze such complex interacting data to reveal the principles of cellular organization, processes and functions. Many studies have shown that clustering protein interaction network is an effective approach for identifying protein complexes or functional modules, which has become a major research topic in systems biology. In this review, recent advances in clustering methods for protein interaction networks will be presented in detail. The predictions of protein functions and interactions based on modules will be covered. Finally, the performance of different clustering methods will be compared and the directions for future research will be discussed.
大规模蛋白质-蛋白质相互作用数据的日益丰富，使得从网络层面理解细胞机制的基本组成和组织成为可能。随之而来的挑战是如何分析这些复杂的相互作用数据，以揭示细胞组织、过程和功能的原理。许多研究表明，对蛋白质相互作用网络进行聚类是识别蛋白质复合物或功能模块的有效方法，这已成为系统生物学的主要研究课题。本文将详细介绍蛋白质相互作用网络聚类方法的最新进展，包括基于模块的蛋白质功能和相互作用预测。最后，将比较不同聚类方法的性能，并讨论未来的研究方向。
