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从MS中提取知识 临床代谢数据:处理和分析策略

Isabel Meister1,2, Julien Boccard1,2, Serge Rudaz3,4

  • 1School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, Geneva, Switzerland.

Methods in molecular biology (Clifton, N.J.)
|October 1, 2024
PubMed
概括

大规模的代谢数据分析对于临床研究至关重要,以确定改变的代谢途径和疾病机制. 分析和生物信息学的进步有助于处理复杂的数据,以发现生物标志物.

关键词:
数据分析, 标注, 评论数据处理数据处理.质谱测量质量谱测量代谢学 代谢学 代谢学

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科学领域:

  • 代谢学 代谢学 代谢学
  • 临床研究 临床研究
  • 生物信息学是一种生物信息学.

背景情况:

  • 大规模的代谢学研究每样产生数千个质量特征.
  • 识别改变的代谢途径是了解疾病机制的关键.
  • 基于质谱 (MS) 的技术是代谢数据生成的核心.

研究的目的:

  • 审查处理和分析复杂的代谢数据的最新发展和挑战.
  • 强调在临床研究中检测改变的途径和发现生物标志物的策略.
  • 为了说明先进分析技术对临床数据的应用.

主要方法:

  • 对质谱数据的峰值检测和样本对齐.
  • 数据规范化和统计分析,以识别重要特征.
  • 使用计算方法进行代谢物注释和生物标志物发现.
  • 复杂数据集的自动化处理和数据分析工作流程.

主要成果:

  • 在分析,生物信息学和化学测量方面取得了重大进展.
  • 自动化工作流改善了大量和复杂的代谢数据的处理.
  • 统计模型越来越多地用于生物标志物发现.

结论:

  • 需要有效的策略来管理和解释大规模的代谢数据.
  • 在临床研究中,数据处理和分析的持续发展是必不可少的.
  • 这些方法有助于阐明病理机制和发现临床生物标志物.