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The Effect of Sequence Complexity on the Construction of Protein-Protein Interaction Networks

机译:序列复杂性对蛋白质-蛋白质相互作用网络构建的影响

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摘要

In this paper, the role of sequence complexity in the construction of important nodes in protein-protein interaction (PPI) networks is investigated. We use two complexity measures, linguistic complexity and Shanon entropy, to measure the complexity of protein sequences. Three different datasets of yeast PPI networks are used to conclude the results. It has been shown that there are two important types of nodes in the PPI networks, which are hub and bottleneck nodes. It has been shown recently that hubs and bottlenecks tend to be essential in the process of evolution. Better understanding of the properties of these two types of nodes will shed light on why proteins interact with each other in the observed manner. We show that the sequence complexity of hubs are lower than that of non-hubs. But the difference is not significant in most cases. On the other hand, the sequence complexity of bottlenecks are lower than that of non-bottlenecks and the difference is significant in most cases. Modularity has an effective role in the construction of PPI networks. We find that there is no significant difference in the node complexity among different modules in a PPI network.
机译:本文研究了序列复杂性在蛋白质-蛋白质相互作用(PPI)网络中重要节点的构建中的作用。我们使用两种复杂性度量(语言复杂性和Shanon熵)来度量蛋白质序列的复杂性。酵母PPI网络的三个不同数据集用于得出结论。已经表明,PPI网络中有两种重要的节点类型,即集线器节点和瓶颈节点。最近显示,枢纽和瓶颈在演进过程中往往至关重要。更好地理解这两种类型的节点的性质将阐明蛋白质为何以观察到的方式相互作用。我们表明,集线器的序列复杂度低于非集线器。但是在大多数情况下,差异并不明显。另一方面,瓶颈的序列复杂度低于非瓶颈的序列复杂度,并且这种差异在大多数情况下都非常明显。模块化在PPI网络的构建中具有有效的作用。我们发现,PPI网络中不同模块之间的节点复杂度没有显着差异。

著录项

  • 来源
    《Brain informatics》|2010年|p.308-319|共12页
  • 会议地点 Toronto(CA);Toronto(CA)
  • 作者

    Mehdi Kargar; Aijun An;

  • 作者单位

    Department of Computer Science and Engineering, York University 4700 Keele Street, Toronto, Ontario, Canada, M3J 1P3;

    Department of Computer Science and Engineering, York University 4700 Keele Street, Toronto, Ontario, Canada, M3J 1P3;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 人工智能理论;
  • 关键词

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