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Prediction of Protein-Protein Interactions Related to Protein Complexes Based on Protein Interaction Networks

机译:基于蛋白质相互作用网络的蛋白质复合物的蛋白质 - 蛋白质相互作用预测

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摘要

A method for predicting protein-protein interactions based on detected protein complexes is proposed to repair deficient interactions derived from high-throughput biological experiments. Protein complexes are pruned and decomposed into small parts based on the adaptive k-cores method to predict protein-protein interactions associated with the complexes. The proposed method is adaptive to protein complexes with different structure, number, and size of nodes in a protein-protein interaction network. Based on different complex sets detected by various algorithms, we can obtain different prediction sets of protein-protein interactions. The reliability of the predicted interaction sets is proved by using estimations with statistical tests and direct confirmation of the biological data. In comparison with the approaches which predict the interactions based on the cliques, the overlap of the predictions is small. Similarly, the overlaps among the predicted sets of interactions derived from various complex sets are also small. Thus, every predicted set of interactions may complement and improve the quality of the original network data. Meanwhile, the predictions from the proposed method replenish protein-protein interactions associated with protein complexes using only the network topology.
机译:提出了一种预测基于检测到的蛋白质复合物的蛋白质 - 蛋白质相互作用的方法,以修复来自高通量生物实验的缺乏相互作用。基于适应性K-Cores方法,将蛋白质复合物修剪并分解成小部件,以预测与复合物相关的蛋白质 - 蛋白质相互作用。该方法适用于蛋白质 - 蛋白质相互作用网络中具有不同结构,数量和节点的尺寸和尺寸的蛋白质复合物。基于各种算法检测到的不同复杂集,我们可以获得不同预测的蛋白质蛋白质相互作用。通过使用统计测试的估计和直接确认生物数据来证明预测交互集的可靠性。与预测基于批变的相互作用的方法相比,预测的重叠是小的。类似地,来自各种复合集的预测相互作用集中的重叠也是小的。因此,每个预测的一组相互作用可以补充和提高原始网络数据的质量。同时,从所提出的方法中的预测仅使用网络拓扑学补充与蛋白质复合物相关的蛋白质 - 蛋白质相互作用。

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