Yu Liang, Gao Lin, Sun Peng Gang
School of Computer Science and Technology, Xidian University, Xi'an, 710071, China.
Int J Data Min Bioinform. 2010;4(5):600-15. doi: 10.1504/ijdmb.2010.035903.
Identifying modules in Protein-Protein Interaction (PPI) networks is important to understand the organisation of the cellular processes. In this paper, we present a novel algorithm combining Molecular Complex Detection (MCODE) with Girvan-Newman (GN) to identify modules in PPI networks. Our algorithm can accurately discover denser modules in large-scale protein interaction networks. We applied it to S. cerevisiae PPI networks and obtained high matching rate between the predicted modules and the known protein complexes in Munich Information Center for Protein Sequences (MIPS). The simulation results show that our algorithm provides an effective, reliable and scalable method of identifying modules in PPI networks.
识别蛋白质-蛋白质相互作用（PPI）网络中的模块对于理解细胞过程的组织至关重要。在本文中，我们提出了一种将分子复合物检测（MCODE）与Girvan-Newman（GN）相结合的新算法，用于识别PPI网络中的模块。我们的算法能够在大规模蛋白质相互作用网络中准确发现更密集的模块。我们将其应用于酿酒酵母PPI网络，并在慕尼黑蛋白质序列信息中心（MIPS）中获得了预测模块与已知蛋白质复合物之间的高匹配率。模拟结果表明，我们的算法为识别PPI网络中的模块提供了一种有效、可靠且可扩展的方法。
