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Protein complex prediction by date hub removal

机译:通过日期中心删除来预测蛋白质复合物

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摘要

Proteins physically interact with each other and form protein complexes to perform their biological functions. The prediction of protein complexes from protein-protein interaction (PPI) network is usually difficult when the complexes are overlapping with each other in a dense region of the network. To address the problem of predicting overlapping complexes, the previously proposed network--decomposition approach is promising. It decomposes a PPI network by e.g. removing proteins with high degree (hubs) which may participate in different complexes. This motivates us to examine a list of proteins, which bind their different partners at a different time or at a different location (viz. date hubs), manually collected from literature, for network decomposition. Results show that the CMC complex discovery algorithm after removing date hubs recalls more overlapping complexes that were missed earlier. Further improvement in performance is achieved when we predict date hub proteins based on simple network features and remove them from PPI networks.
机译:蛋白质彼此物理相互作用并形成蛋白质复合物以执行其生物学功能。当复合物在网络的密集区域中相互重叠时,通常很难从蛋白质-蛋白质相互作用(PPI)网络预测蛋白质复合物。为了解决预测重叠复合物的问题,以前提出的网络分解方法很有希望。它例如通过PPI网络分解。去除可能参与不同复合物的高度蛋白质(集线器)。这促使我们检查一系列蛋白质,这些蛋白质在不同的时间或在不同的位置(即日期中心)与它们的不同伴侣结合,这些蛋白质是从文献中手动收集的,用于网络分解。结果表明,删除日期中心后的CMC复杂物发现算法会召回更多早先遗漏的重叠复杂物。当我们基于简单的网络特征预测日期中心蛋白并将其从PPI网络中删除时,性能会进一步提高。

著录项

  • 作者

    Pyrogova, Iana.;

  • 作者单位

    National University of Singapore (Singapore).;

  • 授予单位 National University of Singapore (Singapore).;
  • 学科 Computer science.;Bioinformatics.
  • 学位 M.S.
  • 年度 2017
  • 页码 80 p.
  • 总页数 80
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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