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Linear time-varying models can reveal non-linear interactions of biomolecular regulatory networks using multiple time-series data

机译:线性时变模型可以使用多个时间序列数据揭示生物分子调控网络的非线性相互作用

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摘要

Motivation: Inherent non-linearities in biomolecular interactions make the identification of network interactions difficult. One of the principal problems is that all methods based on the use of linear time-invariant models will have fundamental limitations in their capability to infer certain non-linear network interactions. Another difficulty is the multiplicity of possible solutions, since, for a given dataset, there may be many different possible networks which generate the same time-series expression profiles.
机译:动机:生物分子相互作用中固有的非线性使得难以识别网络相互作用。主要问题之一是,所有基于使用线性时不变模型的方法,在推断某些非线性网络相互作用的能力方面都将具有根本的局限性。另一个困难是可能的解决方案的多样性,因为对于给定的数据集,可能会有许多不同的可能的网络生成相同的时序表达谱。

著录项

  • 来源
    《Bioinformatics》 |2008年第10期|1286-1292|共7页
  • 作者单位

    Department of Aerospace Engineering University of Glasgow Glasgow G12 8QQ;

    Systems Biology Lab;

    Department of Engineering;

    Department of Biology University of Leicester Leicester LE1 7RH UK and;

    Department of Bio and Brain Engineering and KI for the BioCentury Korea Advanced Institute of Science and Technology (KAIST) 335 Gwahangno Yuseong-gu Daejeon 305-701 Republic of Korea;

  • 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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