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Essential Protein Detection by Random Walk on Weighted Protein-Protein Interaction Networks

机译:通过加权蛋白质-蛋白质相互作用网络上的随机游动进行必需蛋白质检测。

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Essential proteins are critical to the development and survival of cells. Identification of essential proteins is helpful for understanding the minimal set of required genes in a living cell and for designing new drugs. To detect essential proteins, various computational methods have been proposed based on protein-protein interaction (PPI) networks. However, protein interaction data obtained by high-throughput experiments usually contain high false positives, which negatively impacts the accuracy of essential protein detection. Moreover, most existing studies focused on the local information of proteins in PPI networks, while ignoring the influence of indirect protein interactions on essentiality. In this paper, we propose a novel method, called Essentiality Ranking (EssRank in short), to boost the accuracy of essential protein detection. To deal with the inaccuracy of PPI data, confidence scores of interactions are evaluated by integrating various biological information. Weighted edge clustering coefficient (WECC), considering both interaction confidence scores and network topology, is proposed to calculate edge weights in PPI networks. The weight of each node is evaluated by the sum of WECC values of its linking edges. A random walk method, making use of both direct and indirect protein interactions, is then employed to calculate protein essentiality iteratively. Experimental results on the yeast PPI network show that EssRank outperforms most existing methods, including the most commonly-used centrality measures (SC, DC, BC, CC, IC, and EC), topology based methods (DMNC and NC) and the data integrating method IEW.
机译:必需蛋白对于细胞的发育和存活至关重要。必需蛋白质的鉴定有助于理解活细胞中所需基因的最小集合,并有助于设计新药物。为了检测必需蛋白质,已经提出了基于蛋白质-蛋白质相互作用(PPI)网络的各种计算方法。但是,通过高通量实验获得的蛋白质相互作用数据通常包含较高的假阳性,这会对必需蛋白质检测的准确性产生负面影响。而且,大多数现有研究集中在PPI网络中蛋白质的局部信息上,而忽略了间接蛋白质相互作用对必需性的影响。在本文中,我们提出了一种称为必需性排名(EssityRank,简称EssRank)的新方法,以提高必需蛋白检测的准确性。为了处理PPI数据的不准确性,通过整合各种生物学信息来评估相互作用的置信度得分。提出了考虑交互置信度得分和网络拓扑的加权边缘聚类系数(WECC),以计算PPI网络中的边缘权重。每个节点的权重通过其链接边的WECC值之和评估。然后,利用直接和间接蛋白质相互作用的随机游走方法来迭代地计算蛋白质必需性。酵母PPI网络上的实验结果表明,EssRank优于大多数现有方法,包括最常用的集中度度量(SC,DC,BC,CC,IC和EC),基于拓扑的方法(DMNC和NC)以及数据集成方法IEW。

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