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Building blocks of biological networks: a review on major network motif discovery algorithms

机译:生物网络的构建基块:主要网络图案发现算法的综述

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摘要

In recent years, there has been a great interest in studying different aspects of complex networks in a range of fields. One important local property of networks is network motifs, recurrent and statistically significant sub-graphs or patterns, which assists researchers in the identification of functional units in the networks. Although network motifs may provide a deep insight into the network's functional abilities, their detection is computationally challenging. Therefore several algorithms have been introduced to resolve this computationally hard problem. These algorithms can be classified under various paradigms such as exact counting methods, sampling methods, pattern growth methods and so on. Here, the authors will give a review on computational aspects of major algorithms and enumerate their related benefits and drawbacks from an algorithmic perspective.
机译:近年来,在一系列领域中研究复杂网络的不同方面引起了极大的兴趣。网络的一项重要本地属性是网络主题,经常出现且具有统计意义的子图或图案,这有助于研究人员确定网络中的功能单元。尽管网络主题可以提供对网络功能能力的深入了解,但是对它们的检测在计算上却具有挑战性。因此,已经引入了几种算法来解决该计算难题。这些算法可以分类为各种范例,例如精确计数方法,采样方法,模式增长方法等。在这里,作者将对主要算法的计算方面进行回顾,并从算法的角度列举其相关的利弊。

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  • 来源
    《IET systems biology》 |2012年第5期|164-174|共11页
  • 作者单位

    Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Englab sq., 13145-1365, Iran;

    lnstitute of Computer Science, Martin Luther University Halle-Wittenberg, Halle, Germany,Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany;

    Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Englab sq., 13145-1365, Iran;

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  • 入库时间 2022-08-17 13:19:46

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