Cell survival requires the presence of essential proteins. Detection of essential proteins is relevant not only because of the critical biological functions they perform but also the role played by them as a drug target against pathogens. Several computational techniques are in place to identify essential proteins based on protein–protein interaction (PPI) network. Essential protein detection using only physical interaction data of proteins is challenging due to its inherent uncertainty. Hence, in this work, we propose a multiplex network-based framework that incorporates multiple protein interaction data from their physical, coexpression and phylogenetic profiles. An extended version termed as multiplex eigenvector centrality (MEC) is used to identify essential proteins from this network. The methodology integrates the score obtained from the multiplex analysis with subcellular localization and Gene Ontology information and is implemented using Saccharomyces cerevisiae datasets. The proposed method outperformed many recent essential protein prediction techniques in the literature.

中文翻译：

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从蛋白质-蛋白质相互作用的多重网络模型中识别必需蛋白质的综合方法

细胞存活需要必需蛋白质的存在。必需蛋白质的检测之所以重要，不仅是因为它们具有关键的生物学功能，而且还因为它们作为针对病原体的药物靶点所发挥的作用。有几种计算技术可以基于蛋白质-蛋白质相互作用 (PPI) 网络来识别必需蛋白质。由于其固有的不确定性，仅使用蛋白质的物理相互作用数据进行基本蛋白质检测具有挑战性。因此，在这项工作中，我们提出了一个基于多重网络的框架，该框架结合了来自其物理、共表达和系统发育谱的多种蛋白质相互作用数据。称为多重特征向量中心性 (MEC) 的扩展版本用于从该网络中识别基本蛋白质。该方法将从多重分析中获得的分数与亚细胞定位和基因本体信息相结合，并使用酿酒酵母数据集实施。所提出的方法优于文献中许多最近的基本蛋白质预测技术。