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Fast protein network clustering algorithm: a new approach for clustering protein-protein interaction networks to detect functional modules

机译:快速蛋白质网络聚类算法:一种用于蛋白质 - 蛋白质相互作用网络检测功能模块的新方法

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Proteins are responsible for health disorder, evolution of species. These tasks are carried out by forming functional modules. Each functional module possesses community structure. For identifying the functional modules, a lot of community detection or clustering algorithms are designed, but most of the algorithms suffer by either high computational time or inappropriate clustering results. We propose an algorithm based on relative vertex-to-vertex clustering value and agglomerative hierarchical method, known as Fast Protein Network Clustering algorithm. It is faster than existing algorithms and resolves the most common clustering problem-clustering any vertex (protein) of degree one from its neighbour. We also tested our algorithm with the most popular four algorithms in respect to functional module mapping and efficiency analysis. FPNC algorithm successfully outperformed existing algorithms which is the current state-of-the-art agglomerative approach to functional module identification.
机译:蛋白质负责健康障碍,物种的演变。这些任务是通过形成功能模块来执行的。每个功能模块都具有社区结构。为了识别功能模块,设计了许多社区检测或聚类算法,但大多数算法受到高计算时间或不适当的群集结果的影响。我们提出了一种基于相对顶点到顶点聚类价值和附聚层方法的算法,称为快速蛋白网络聚类算法。它比现有算法快,并解决了来自其邻居的一个顶点(蛋白)的最常见的聚类问题。我们还通过了解功能模块映射和效率分析,通过最受欢迎的四种算法测试了我们的算法。 FPNC算法成功优于现有的算法,该算法是功能模块识别的当前最先进的附注方法。

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