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Comparing the biological coherence of network clusters identified by different detection algorithms

机译：比较不同检测算法识别的网络集群的生物一致性

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摘要

Protein-protein interaction networks serve to carry out basic molecular activity in the cell. Detecting the modular structures from the protein-protein interaction network is important for understanding the organization, function and dynamics of a biological system. In order to identify functional neighborhoods based on network topology, many network cluster identification algorithms have been developed. However, each algorithm might dissect a network from a different aspect and may provide differentinsight on the network partition. In order to objectively evaluate the performance of four commonly used cluster detection algorithms: molecular complex detection (MCODE), NetworkBlast, shortest-distance clustering (SDC) and Girvan-Newman (G-N) algorithm, we compared the biological coherence of the network clusters found by these algorithms through a uniform evaluation framework. Each algorithm was utilized to find network clusters in two different protein-protein interaction networks with various parameters. Comparison of the resulting network clusters indicates that clusters found by MCODE and SDC are of higher biological coherence than those by NetworkBlast and G-N algorithm.
机译：蛋白质-蛋白质相互作用网络用于在细胞中进行基本的分子活性。从蛋白质-蛋白质相互作用网络中检测模块结构对于理解生物系统的组织，功能和动力学非常重要。为了基于网络拓扑识别功能邻域，已经开发了许多网络集群识别算法。但是，每种算法都可能从不同的角度剖析网络，并且可能会对网络分区提供不同的见解。为了客观地评估四种常用群集检测算法的性能：分子复合物检测（MCODE），NetworkBlast，最短距离群集（SDC）和Girvan-Newman（GN）算法，我们比较了发现的网络群集的生物一致性由这些算法通过一个统一的评估框架。利用每种算法在两个具有不同参数的不同蛋白质-蛋白质相互作用网络中查找网络簇。比较所得到的网络群集，表明通过MCODE和SDC找到的群集具有比NetworkBlast和G-N算法更高的生物学一致性。

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