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Common Neighbors Extension of the Sticky Model for PPI Networks Evaluated by Global and Local Graphlet Similarity

机译:通过全球和本地石墨相似性评估的PPI网络粘性模型的常见邻居扩展

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The structure of protein-protein interaction (PPI) networks has been studied for over a decade. Many theoretical models have been proposed to model PPI network structure, but continuing noise and incompleteness in these networks make conclusions about their structure difficult. Using newer, larger networks from Sept. 2018 BioGRID and Jan. 2019 IID, we show the joint distribution of degree products and common neighbors has a greater impact on PPI edge connectivity than their individual distributions, and introduce two new models (CN and STICKY-CN) for PPI networks employing these features. Since graphlet-based measures are believed to be among the most discerning and sensitive network comparison tools available, we assess their overall global and local fits to PPI networks using Graphlet Kernel (GK). We fit 10 theoretical models to nine BioGRID networks and twelve Integrated Interactive Database (IID) networks and find: (1) STICKY and STICKY-CN are the overall globally best fitting models according to GK, (2) Hyperbolic Geometric Graph model is a better fit than any STICKY-based model on 4 species, (3) though STICKY-CN provides a better local fit than the STICKY model, the CN model provides the greatest local fit over most species. We conclude that the inclusion of CN into STICKY-CN makes it the best overall fit for PPI networks as it is a good fit locally and globally.
机译:多年来研究了蛋白质 - 蛋白质相互作用(PPI)网络的结构。已经提出了许多理论模型来模拟PPI网络结构,但在这些网络中持续的噪声和不完整性地结论它们的结构困难。从2018年9月的EID和2019年1月开始使用较新的网络,我们展示了学位产品的联合分配和普通邻居对PPI边缘连接的影响比各个分布更大,并引入了两个新型号(CN和粘性 - CN)用于采用这些功能的PPI网络。由于基于Graphlet的措施,据信是可用的最挑剔和敏感的网络比较工具中,我们使用Graphlet内核(GK)评估其整体全局和本地适合PPI网络。我们将10个理论模型适合九个生物格栅网络和12个综合交互式数据库(IID)网络,找到:(1)粘性和粘性-CN是根据GK的整体全球最佳拟合模型,(2)双曲几何图模型是更好的适合于4种的任何粘性模型,(3)虽然粘性-CN提供比粘性模型更好的本地合适,但CN模型提供了最伟大的本地拟合大多数物种。我们得出结论,将CN纳入粘性-CN使其成为PPI网络的最佳整体适合,因为它是局部和全球的良好。

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