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Identifying Spurious Interactions and Predicting Missing Interactions in the Protein-Protein Interaction Networks via a Generative Network Model

机译:通过生成网络模型识别杂散相互作用并预测蛋白质-蛋白质相互作用网络中的缺失相互作用

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With the rapid development of high-throughput experiment techniques for protein-protein interaction (PPI) detection, a large amount of PPI network data are becoming available. However, the data produced by these techniques have high levels of spurious and missing interactions. This study assigns a new reliably indication for each protein pairs via the new generative network model (RIGNM) where the scale-free property of the PPI network is considered to reliably identify both spurious and missing interactions in the observed high-throughput PPI network. The experimental results show that the RIGNM is more effective and interpretable than the compared methods, which demonstrate that this approach has the potential to better describe the PPI networks and drive new discoveries.
机译:随着用于蛋白质-蛋白质相互作用(PPI)检测的高通量实验技术的迅速发展,大量的PPI网络数据变得可用。但是,通过这些技术生成的数据具有高水平的虚假交互和缺失交互。这项研究通过新的生成网络模型(RIGNM)为每个蛋白质对分配了新的可靠指示,其中PPI网络的无标度特性被认为可以可靠地识别观察到的高通量PPI网络中的虚假相互作用和缺失相互作用。实验结果表明,相比于比较方法,RIGNM更有效,更可解释,这表明该方法具有更好地描述PPI网络并推动新发现的潜力。

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