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MFC: Mining Maximal Frequent Dense Subgraphs without Candidate Maintenance in Imbalanced PPI Networks

机译:MFC:在不平衡的PPI网络中挖掘没有候选维护的最大频繁密集子图

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摘要

The prediction of protein function is one of the most challenging problems in bioinformatics. Several studies have shown that the prediction using PPI is promising. However, the PPI data generated from high-throughput experiments are very noisy, which renders great challenges to the existing methods. In this paper, we propose an algorithm, MFC, to efficiently mine maximal frequent dense subgraphs without candidate maintenance in PPI networks. Instead of using summary graph, MFC produces frequent dense patterns by extending vertices. It adopts several techniques to achieve efficient mining. Due to the imbalance character of PPI network, we also propose to generate frequent patterns using relative support. We evaluate our approach on four PPI data sets. The experimental results show that our approach has good performance in terms of efficiency. With the help of relative support, more frequent dense functional interaction patterns in the PPI networks can be identified.
机译:蛋白质功能的预测是生物信息学中最具挑战性的问题之一。多项研究表明,使用PPI进行预测是有希望的。但是,高通量实验产生的PPI数据非常嘈杂,这给现有方法带来了巨大挑战。在本文中,我们提出了一种算法MFC,可以有效地挖掘最大频繁密集子图,而无需在PPI网络中进行候选维护。 MFC不使用汇总图,而是通过扩展顶点来生成频繁的密集图案。它采用多种技术来实现有效的挖掘。由于PPI网络的不平衡特性,我们还建议使用相对支持来生成频繁模式。我们评估了四个PPI数据集的方法。实验结果表明,我们的方法在效率方面具有良好的性能。借助相对支持,可以确定PPI网络中更频繁的密集功能交互模式。

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