In this work, we introduce centrality metrics based on group structures, and we show their performance in estimating importance in protein-protein interaction networks (PPINs). The centrality metrics introduced are extensions of well-known nodal metrics. However, instead of focusing on a single node, we focus on that node and the set of nodes around it. Furthermore, we require the set of nodes to induce a specific pattern or structure. The structures investigated range from the “stricter“ induced stars and cliques, to a “looser” definition of a representative structure. We derive the computational complexity of all metrics and provide mixed integer programming formulations; due to the problem complexity and the size of PPINs, using commercial solvers is not always viable. Hence, we propose a combinatorial branch-and-bound solution approach. We conclude by showing the effectiveness of the proposed metrics in identifying essential proteins in Helicobacter pylori and comparing them to nodal metrics.

中文翻译：

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基于组结构研究蛋白质-蛋白质相互作用网络重要性的新中心性度量

在这项工作中，我们引入了基于组结构的中心性度量，并展示了它们在估计蛋白质-蛋白质相互作用网络 (PPIN) 中的重要性方面的表现。引入的中心性度量是众所周知的节点度量的扩展。但是，我们不是关注单个节点，而是关注该节点及其周围的节点集。此外，我们需要一组节点来诱导特定的模式或结构。研究的结构范围从“更严格”的诱导恒星和集团，到“更宽松”的代表性结构定义。我们推导出所有度量的计算复杂度，并提供混合整数规划公式；由于问题的复杂性和 PPIN 的大小，使用商业求解器并不总是可行的。因此，我们提出了一种组合分支定界解决方案方法。幽门螺杆菌并将它们与淋巴结指标进行比较。