Essential protein plays a vital role in understanding the cellular life. With the advance in high-throughput technologies, a number of protein-protein interaction (PPI) networks have been constructed such that essential proteins can be identified from a system biology perspective. Although a series of network-based essential protein discovery methods have been proposed, these existing methods still have some drawbacks. Recently, it has been shown that the significance-based method SigEP is promising on overcoming the defects that are inherent in currently available essential protein identification methods. However, the SigEP method is developed under the unrealistic Erdös-Rényi (E-R) model and its time complexity is very high. Hence, we propose a new significance-based essential protein recognition method named EPCS in which the essential protein discovery problem is formulated as a community significance testing problem. Experimental results on four PPI networks show that EPCS performs better than nine state-of-the-art essential protein identification methods and the only significance-based essential protein identification method SigEP.

中文翻译：

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通过社区意义识别基本蛋白质

必需蛋白质在了解细胞生命方面起着至关重要的作用。随着高通量技术的进步，已经构建了许多蛋白质-蛋白质相互作用（PPI）网络，以便可以从系统生物学的角度识别必需的蛋白质。尽管已经提出了一系列基于网络的必需蛋白发现方法，但这些现有方法仍然存在一些缺点。最近，已经表明基于重要性的方法 SigEP 有望克服目前可用的必需蛋白质鉴定方法中固有的缺陷。然而，SigEP 方法是在不切实际的 Erdös-Rényi (ER) 模型下开发的，其时间复杂度非常高。因此，我们提出了一种新的基于重要性的必需蛋白质识别方法，称为 EPCS，其中必需蛋白质发现问题被表述为社区重要性测试问题。在四个 PPI 网络上的实验结果表明，EPCS 的性能优于九种最先进的必需蛋白鉴定方法和唯一基于显着性的必需蛋白鉴定方法 SigEP。