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Graphlet-based edge clustering reveals pathogen-interacting proteins

机译:基于图谱的边缘聚类揭示了病原体相互作用蛋白

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摘要

Motivation: Prediction of protein function from protein interaction networks has received attention in the post-genomic era. A popular strategy has been to cluster the network into functionally coherent groups of proteins and assign the entire cluster with a function based on functions of its annotated members. Traditionally, network research has focused on clustering of nodes. However, clustering of edges may be preferred: nodes belong to multiple functional groups, but clustering of nodes typically cannot capture the group overlap, while clustering of edges can. Clustering of adjacent edges that share many neighbors was proposed recently, outperforming different node clustering methods. However, since some biological processes can have characteristic ‘signatures’ throughout the network, not just locally, it may be of interest to consider edges that are not necessarily adjacent.
机译:动机:在后基因组时代,从蛋白质相互作用网络预测蛋白质功能已受到关注。一种流行的策略是将网络聚类为功能上一致的蛋白质组,并根据其带注释成员的功能为整个聚类分配功能。传统上,网络研究集中于节点的群集。但是,边缘的群集可能更可取:节点属于多个功能组,但是节点的群集通常无法捕获组重叠,而边缘的群集却可以。最近提出了共享许多邻居的相邻边缘的聚类,其性能优于不同的节点聚类方法。但是,由于某些生物过程可能在整个网络中都具有特征性的“签名”,而不仅是局部的,因此考虑不一定相邻的边缘可能会很有意义。

著录项

  • 来源
    《Bioinformatics》 |2012年第18期|p.480-486|共7页
  • 作者单位

    Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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