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Industrial methodology for process verification in research (IMPROVER): toward systems biology verification

机译:研究过程验证的工业方法论(IMPROVER):系统生物学验证

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摘要

Motivation: Analyses and algorithmic predictions based on high-throughput data are essential for the success of systems biology in academic and industrial settings. Organizations, such as companies and academic consortia, conduct large multi-year scientific studies that entail the collection and analysis of thousands of individual experiments, often over many physical sites and with internal and outsourced components. To extract maximum value, the interested parties need to verify the accuracy and reproducibility of data and methods before the initiation of such large multi-year studies. However, systematic and well-established verification procedures do not exist for automated collection and analysis workflows in systems biology which could lead to inaccurate conclusions.
机译:动机:基于高通量数据的分析和算法预测对于系统生物学在学术和工业环境中的成功至关重要。公司和学术团体等组织进行大型的多年科学研究,这些研究通常需要在许多物理站点上以及内部和外包组件中进行成千上万次单独实验的收集和分析。为了获得最大价值,有关方面需要在开展如此大规模的多年研究之前验证数据和方法的准确性和可重复性。但是,系统生物学中不存在用于自动收集和分析工作流的系统且完善的验证程序,这可能导致不正确的结论。

著录项

  • 来源
    《Bioinformatics》 |2012年第9期|p.1193-1201|共9页
  • 作者

    Gustavo Stolovitzky;

  • 作者单位
  • 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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