The identification of essential proteins in protein-protein interaction (PPI) networks is not only important in understanding the process of cellular life but also useful in diagnosis and drug design. The network topology-based centrality measures are sensitive to noise of network. Moreover, these measures cannot detect low-connectivity essential proteins. The authors have proposed a new method using a combination of topological centrality measures and biological features based on statistical analyses of essential proteins and protein complexes. With incomplete PPI networks, they face the challenge of false-positive interactions. To remove these interactions, the PPI networks are weighted by gene ontology. Furthermore, they use a combination of classifiers, including the newly proposed measures and traditional weighted centrality measures, to improve the precision of identification. This combination is evaluated using the logistic regression model in terms of significance levels. The proposed method has been implemented and compared to both previous and more recent efficient computational methods using six statistical standards. The results show that the proposed method is more precise in identifying essential proteins than the previous methods. This level of precision was obtained through the use of four different data sets: YHQ-W, YMBD-W, YDIP-W and YMIPS-W.

中文翻译：

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基于加权蛋白质-蛋白质相互作用网络中拓扑和生物学特征的新组合识别必需蛋白质。

蛋白质-蛋白质相互作用（PPI）网络中必需蛋白质的鉴定不仅对于理解细胞生命过程很重要，而且对于诊断和药物设计也很有用。基于网络拓扑的中心性度量对网络噪声敏感。此外，这些措施无法检测低连接性必需蛋白质。作者基于必需蛋白质和蛋白质复合物的统计分析，提出了一种结合拓扑中心性度量和生物学特征的新方法。由于 PPI 网络不完整，他们面临着误报交互的挑战。为了消除这些相互作用，PPI 网络通过基因本体进行加权。此外，他们使用分类器的组合，包括新提出的措施和传统的加权中心性措施，以提高识别的精度。使用逻辑回归模型根据显着性水平评估该组合。所提出的方法已经实施，并使用六个统计标准与以前和最近的有效计算方法进行了比较。结果表明，所提出的方法在识别必需蛋白质方面比以前的方法更精确。这种精度水平是通过使用四个不同的数据集获得的：YHQ-W、YMBD-W、YDIP-W 和 YMIPS-W。