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FLOW MODEL OF THE PROTEIN-PROTEIN INTERACTION NETWORK FOR FINDING CREDIBLE INTERACTIONS

机译:蛋白质 - 蛋白质相互作用网络的流量模型,用于寻找可信相互作用

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Large-scale protein-protein interactions (PPIs) detected by yeast-two-hybrid (Y2H) systems are known to contain many false positives. The separation of credible interactions from background noise is still an unavoidable task. In the present study, wepropose the relative reliability score for PPI as an intrinsic characteristic of global topology in the PPI networks. Our score is calculated as the dominant eigenvector of an adjacency matrix and represents the steady state of the network flow. By usingthis reliability score as a cut-off threshold from noisy Y2H PPI data, the credible interactions were extracted with better or comparable performance of previously proposed methods which were also based on the network topology. The result suggests thatthe application of the network-flow model to PPI data is useful for extracting credible interactions from noisy experimental data.
机译:已知由酵母 - 双杂交(Y2H)系统检测的大规模蛋白质 - 蛋白质相互作用(PPI)包含许多误报。与背景噪声的可信相互作用的分离仍然是一个不可避免的任务。在本研究中,Wepropose PPI的相对可靠性分数作为PPI网络中全球拓扑的内在特征。我们的分数被计算为邻接矩阵的主导特征向量,代表网络流的稳定状态。通过使用从嘈杂的Y2H PPI数据中作为截止阈值的可靠性得分,通过更好或类似的方法提取可信的相互作用,其先前提出的方法也基于网络拓扑。结果表明,网络流模型将网络流模型应用于PPI数据对于从嘈杂的实验数据中提取可靠的相互作用是有用的。

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