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LOCANDA: Exploiting Causality in the Reconstruction of Gene Regulatory Networks

机译：洛坎达：利用因果关系重建基因调控网络

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摘要

The reconstruction of gene regulatory networks via link prediction methods is receiving increasing attention due to the large availability of data, mainly produced by high throughput technologies. However, the reconstructed networks often suffer from a high amount of false positive links, which are actually the result of indirect regulation activities. Such false links are mainly due to the presence of common cause and common effect phenomena, which are typically present in gene regulatory networks. Existing methods for the identification of a transitive reduction of a network or for the removal of (possibly) redundant links suffer from limitations about the structure of the network or the nature/length of the indirect regulation, and often require additional pre-processing steps to handle specific peculiarities of the networks at hand (e.g., cycles). In this paper, we propose the method LOCANDA, which overcomes these limitations and is able to identify and exploit indirect relationships of arbitrary length to remove links considered as false positives. This is performed by identifying indirect paths in the network and by comparing their reliability with that of direct links. Experiments performed on networks of two organisms (E. coli and S. cerevisiae) show a higher accuracy in the reconstruction with respect to the considered competitors, as well as a higher robustness to the presence of noise in the data.
机译：由于主要由高通量技术产生的数据的大量可用性，通过链接预测方法重建基因调控网络受到越来越多的关注。然而，重建的网络经常遭受大量的误报链接，这实际上是间接监管活动的结果。此类错误链接主要是由于共同原因和共同效应现象的存在，这些现象通常存在于基因调节网络中。现有的用于识别网络的过渡性减少或用于（可能）冗余链路的移除的方法受到网络结构或间接法规的性质/长度的限制，并且通常需要额外的预处理步骤来处理手头网络的特定特性（例如循环）。在本文中，我们提出了LOCANDA方法，该方法克服了这些限制，并且能够识别和利用任意长度的间接关系来删除被视为误报的链接。这是通过识别网络中的间接路径并将其可靠性与直接链路的可靠性进行比较来执行的。在两种生物（大肠杆菌和酿酒酵母）的网络上进行的实验表明，相对于所考虑的竞争者，重构的准确性更高，并且对数据中存在的噪声具有更高的鲁棒性。

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  • 来源
    《Discovery science》|2017年|283-297|共15页
  • 会议地点 Kyoto(JP)
  • 作者

    Gianvito Pio; Michelangelo Ceci; Francesca Prisciandaro; Donato Malerba;

  • 作者单位

    Department of Computer Science, University of Bari Aldo Moro, Via Orabona, 4, 70125 Bari, Italy;

    Department of Computer Science, University of Bari Aldo Moro, Via Orabona, 4, 70125 Bari, Italy;

    Department of Computer Science, University of Bari Aldo Moro, Via Orabona, 4, 70125 Bari, Italy;

    Department of Computer Science, University of Bari Aldo Moro, Via Orabona, 4, 70125 Bari, Italy;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Causality; Bionformatics; Gene network reconstruction;

    机译：因果关系；生物信息学基因网络重建;

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