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Protein complex detection from PPI networks on Apache Spark

机译:来自PPI网络的蛋白质复杂检测Apache Spark

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Protein-Protein Interaction (PPI) network is a network of biomolecular interactions which plays a major role in modeling and analyzing biological activities. Studies of functional modules from PPI networks provide a better understanding of biological mechanisms. Recent advances in both biological and computer sciences demands for the vast amount of PPI networks data to be processed by experimental and computational methods. This could be a great challenge to find functional modules within these large networks. Existing methods are used to identify the functional modules, but some of them do not consider overlapping between functional module clusters. Moreover, most of the methods run on a single machine. Also, many existing algorithms only focus on topological features of PPI networks. In this paper, we introduce a new way for detecting the functional modules. It considers overlapping between clusters and runs on Apache Spark - a distributed processing platform. Our algorithm also considers both topological and biological features of PPI networks. The evaluation results show improved execution speed as well as more accurate results compared to classic methods.
机译:蛋白质 - 蛋白质相互作用(PPI)网络是生物分子相互作用网络,其在建模和分析生物活动中发挥着重要作用。 PPI网络功能模块的研究提供了更好地了解生物机制。生物和计算机科学的最近进展要求通过实验和计算方法处理大量PPI网络数据。这可能是在这些大型网络中找到功能模块的巨大挑战。现有方法用于识别功能模块,但其中一些不考虑功能模块集群之间的重叠。而且,大多数方法在一台机器上运行。此外,许多现有算法仅关注PPI网络的拓扑功能。在本文中,我们介绍了一种用于检测功能模块的新方法。它考虑了群集之间的重叠,并在Apache Spark - 一个分布式处理平台上运行。我们的算法还考虑了PPI网络的拓扑和生物学特征。与经典方法相比,评估结果显示出改善的执行速度以及更准确的结果。

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