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首页> 外文期刊>Statistical methods in medical research >A big data pipeline: Identifying dynamic gene regulatory networks from time-course Gene Expression Omnibus data with applications to influenza infection
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A big data pipeline: Identifying dynamic gene regulatory networks from time-course Gene Expression Omnibus data with applications to influenza infection

机译：一个大数据管道：用时间课程基因表达综合征鉴定动态基因调节网络，其与流感感染的应用

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摘要

A biological host response to an external stimulus or intervention such as a disease or infection is a dynamic process, which is regulated by an intricate network of many genes and their products. Understanding the dynamics of this gene regulatory network allows us to infer the mechanisms involved in a host response to an external stimulus, and hence aids the discovery of biomarkers of phenotype and biological function. In this article, we propose a modeling/analysis pipeline for dynamic gene expression data, called Pipeline4DGEData, which consists of a series of statistical modeling techniques to construct dynamic gene regulatory networks from the large volumes of high-dimensional time-course gene expression data that are freely available in the Gene Expression Omnibus repository. This pipeline has a consistent and scalable structure that allows it to simultaneously analyze a large number of time-course gene expression data sets, and then integrate the results across different studies. We apply the proposed pipeline to influenza infection data from nine studies and demonstrate that interesting biological findings can be discovered with its implementation.
机译：对外部刺激或干预的生物宿主反应，例如疾病或感染是一种动态过程，其由许多基因及其产品的复杂网络调节。了解该基因监管网络的动态使我们能够推断对外部刺激的宿主响应中涉及的机制，因此有助于发现表型和生物学功能的生物标志物。在本文中，我们提出了一种用于动态基因表达数据的建模/分析管道，称为Pipeline4DgeData，其包括一系列统计建模技术，用于构建来自大量的高尺寸时间过程基因表达数据的动态基因调节网络在基因表达式omnibus存储库中自由使用。该管道具有一致且可扩展的结构，允许它同时分析大量的时间课程基因表达数据集，然后将结果集成在不同的研究中。我们将拟议的管道应用于来自九项研究的流感感染数据，并证明可以通过其实施发现有趣的生物学发现。

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