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Centrality Measures in Biological Networks

机译:生物网络中的集中度度量

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摘要

Many complex systems such as biological and social systems can be modeled using graph structures called biological networks and social networks. Instead of studying separately each of the elements composing such complex systems, it is easier to study the networks representing the interactions between the elements of these systems. A commonly known fact in biological and social networks' analysis is that in most networks some important or influential elements (e.g. essential proteins in PPI networks) are placed in some particular positions in a network. These positions (i.e. vertices) have some particular structural properties. Centrality measures quantify such facts from different points of view. Based on centrality measures the graph elements such as vertices and edges can be ranked from different points of view. Top ranked elements in the graph are supposed to play an important role in the network. This paper presents a comprehensive review of existing different centrality measures and their applications in some biological networks such as Protein-Protein interaction network, residue interaction and gene-gene interaction networks.
机译:可以使用称为生物网络和社交网络的图结构来建模许多复杂的系统,例如生物和社会系统。与其单独研究组成此类复杂系统的每个元素,不如研究代表这些系统元素之间相互作用的网络。生物和社交网络分析中的一个众所周知的事实是,在大多数网络中,一些重要或有影响力的元素(例如PPI网络中的必需蛋白质)被放置在网络中的某些特定位置。这些位置(即顶点)具有某些特殊的结构特性。集中度度量从不同角度量化了这些事实。基于中心度度量,可以从不同的角度对图形元素(如顶点和边)进行排名。图中排名最高的元素应该在网络中发挥重要作用。本文对现有的不同集中度测量方法及其在某些生物网络中的应用进行了全面的综述,例如蛋白质-蛋白质相互作用网络,残基相互作用和基因-基因相互作用网络。

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