Kuchaiev Oleksii, Przulj Natasa
Department of Computer Science, University of California, Irvine, CA 92697-3425, USA.
Pac Symp Biocomput. 2009:39-50.
Modeling and analyzing protein-protein interaction (PPI) networks is an important problem in systems biology. Many random graph models were proposed to capture specific network properties or mimic the way real PPI networks might have evolved. In this paper we introduce a new generative model for PPI networks which is based on geometric random graphs and uses the whole connectivity information of the real PPI networks to learn their structure. Using only the high confidence part of yeast S. cerevisiae PPI network for training our new model, we successfully reproduce structural properties of other lower-confidence yeast, as well as of human PPI networks coming from different data sources. Thus, our new approach allows us to utilize high quality parts of currently available PPI data to create accurate models for PPI networks of different species.
对蛋白质-蛋白质相互作用（PPI）网络进行建模和分析是系统生物学中的一个重要问题。人们提出了许多随机图模型来捕捉特定的网络特性或模拟真实PPI网络可能的演化方式。在本文中，我们介绍了一种新的PPI网络生成模型，该模型基于几何随机图，并利用真实PPI网络的整体连通性信息来学习其结构。仅使用酿酒酵母PPI网络的高置信度部分来训练我们的新模型，我们成功地重现了其他低置信度酵母以及来自不同数据源的人类PPI网络的结构特性。因此，我们的新方法使我们能够利用当前可用PPI数据的高质量部分，为不同物种的PPI网络创建准确的模型。
