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Improved approach for protein function prediction by exploiting prominent proteins

机译:通过利用重要蛋白质来预测蛋白质功能的改进方法

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Protein-protein interaction (PPI) networks are valuable biological data source which contain rich information useful for protein function prediction. The PPI network data set obtained from high-throughput experiments is known to be noisy and incomplete. By modeling PPI data as a graph, research efforts are being made in the literature to improve the performance of protein function prediction by extending common neighbor, clustering, and classification based approaches. These approaches exploit the fact that protein shares function with other proteins which are connected through common neighbours. As PPI data is modeled as a graph, it contains prominent nodes which establish relatively high connectivity with other modes. In this paper we propose an improved approach for protein function prediction by exploiting the connectivity properties of prominent proteins. Experimental results on real-world data sets demonstrate the effectiveness of proposed approach.
机译:蛋白质-蛋白质相互作用(PPI)网络是有价值的生物学数据源,其中包含对蛋白质功能预测有用的丰富信息。从高通量实验获得的PPI网络数据集是嘈杂且不完整的。通过将PPI数据建模为图形,文献中的研究工作是通过扩展基于通用邻居,聚类和分类的方法来提高蛋白质功能预测的性能。这些方法利用了蛋白质与通过共同邻居连接的其他蛋白质共享功能的事实。由于PPI数据被建模为图形,因此它包含突出的节点,这些节点与其他模式建立了相对较高的连通性。在本文中,我们提出了一种通过利用突出蛋白质的连接特性来预测蛋白质功能的改进方法。在真实数据集上的实验结果证明了该方法的有效性。

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