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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >Detecting Protein Complexes Basedon Uncertain Graph Model
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Detecting Protein Complexes Basedon Uncertain Graph Model

机译：基于不确定图模型的蛋白质复合物检测

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摘要

Advanced biological technologies are producing large-scale protein–protein interaction (PPI) data at an ever increasing pace, which enable us to identify protein complexes from PPI networks. Pair-wise protein interactions can be modeled as a graph, where vertices represent proteins and edges represent PPIs. However most of current algorithms detect protein complexes based on deterministic graphs, whose edges are either present or absent. Neighboring information is neglected in these methods. Based on the uncertain graph model, we propose the concept of expected density to assess the density degree of a subgraph, the concept of relative degree to describe the relationship between a protein and a subgraph in a PPI network. We develop an algorithm called DCU (detecting complex based on uncertain graph model) to detect complexes from PPI networks. In our method, the expected density combined with the relative degree is used to determine whether a subgraph represents a complex with high cohesion and low coupling. We apply our method and the existing competing algorithms to two yeast PPI networks. Experimental results indicate that our method performs significantly better than the state-of-the-art methods and the proposed model can provide more insights for future study in PPI networks.
机译：先进的生物技术正以越来越快的速度生成大规模的蛋白质-蛋白质相互作用（PPI）数据，这使我们能够从PPI网络中识别蛋白质复合物。可以将成对蛋白质相互作用建模为图形，其中顶点代表蛋白质，边缘代表PPI。但是，当前大多数算法都基于确定性图来检测蛋白质复合物，该图的边缘存在或不存在。这些方法忽略了相邻的信息。基于不确定图模型，我们提出了期望密度的概念来评估子图的密度度，提出相对密度的概念来描述蛋白质和子图在PPI网络中的关系。我们开发了一种称为DCU（基于不确定图模型的检测复合物）的算法来检测PPI网络中的复合物。在我们的方法中，将预期密度与相对度相结合来确定子图是否表示具有高内聚和低耦合的复合物。我们将我们的方法和现有的竞争算法应用于两个酵母PPI网络。实验结果表明，我们的方法的性能明显优于最新方法，所提出的模型可以为将来在PPI网络中的研究提供更多见识。

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