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机器学习分析大代谢数据来分类抑郁症:模型开发和验证

Simeng Ma1, Xinhui Xie1, Zipeng Deng1

  • 1Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China.

Biological psychiatry
|December 24, 2023
PubMed
概括

这项研究使用机器学习在英国大型生物库数据集中确定了24种与抑郁症相关的代谢生物标志物. 这些发现可能有助于开发用于早期抑郁症检测和理解其机制的新工具.

关键词:
生物标志物 生物标志物抑郁症 抑郁症 抑郁症机器学习 机器学习代谢学 代谢学 代谢学英国生物银行怀特霍尔 II 队列的队列.

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科学领域:

  • 计算生物学是一种计算生物学.
  • 代谢学 代谢学 代谢学
  • 精神病学研究精神病学研究

背景情况:

  • 以前的抑郁症代谢学研究因规模而受到限制.
  • 对代谢物水平的大规模in silico分析可以提供对抑郁症病理学和生物标志物的见解.

研究的目的:

  • 在大群体中对全球代谢物水平进行全面的in silico分析,以确定与抑郁症相关的代谢生物标志物.
  • 探索这些生物标志物用于抑郁症检测的潜在临床应用.

主要方法:

  • 使用了两个英国生物库数据集 (N=123,459对终身抑郁症,N=94,921对当前抑郁症) 和怀特霍尔II队列进行验证.
  • 采用CatBoost机器学习用于建模和Shapley增量解释解释,具有五倍交叉验证.
  • 通过使用接收器操作特征曲线下的面积来评估诊断性能.

主要成果:

  • 在数据集中确定了24种与抑郁症有显著关联的代谢生物标志物,其中12种重叠.
  • 包括代谢特征在内略有改善了诊断模型的性能 (例如,终身抑郁症的AUC从0.655增加到0.658).

结论:

  • 一个机器学习模型成功识别了24个与抑郁症相关的代谢生物标志物.
  • 经过验证的代谢生物标志物可能会补充早期,基于人口的抑郁症查的传统风险因素.