使用联合MALDI MS和机器学习方法进行液体活检生物型分析的端到端工作流程
Lukáš Pečinka1,2, Jaromíra Pantůčková3, Monika Vlachová4
1Research Centre for Applied Molecular Oncology (RECAMO), Masaryk Memorial Cancer Institute, Žlutý Kopec 7, Brno 60200, Czech Republic.
Analytical methods : advancing methods and applications
|December 5, 2025
概括
带有机器学习 (ML) 的矩阵辅助激光脱/电离质谱 (MALDI MS) 提供了非侵入性疾病查. 本研究引入了用于液体活检分析的开源R工作流,提高了可复制性和临床整合性.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 在瘤学瘤学.
背景情况:
- 使用MALDI MS的液体活检分析显示了对非侵入性疾病检测的前景.
- 机器学习集成可以提高MALDI MS数据的预测能力.
- 这些技术的临床翻译需要标准化的工作流程.
研究的目的:
- 介绍一个基于R的开源工作流,用于端到端的MALDI MS液体活检分析.
- 为了实现可定制和透明的数据预处理和预测模型评估.
- 为了验证血液瘤疾病临床样本的工作流程.
主要方法:
- 为 MALDI MS 数据开发一个基于 R 的计算管道.
- 实施数据预处理步骤,包括规范化和特征提取.
- 机器学习模型用于疾病分类和监测的应用.
- 使用来自血液瘤患者的血样本进行验证.
主要成果:
- 该R工作流提供了一个全面和可定制的管道,用于马尔迪MS液体活检分析.
- 工作流程证明了成功的数据预处理和预测模型评估.
- 临床样本的验证证实了血液瘤患者监测的实用性.
- 通过标准化工作流程实现了增强的数据可重复性.
结论:
- 本文所介绍的开源工作流程有助于整合MALDI的MS和ML用于非侵入性疾病查.
- 这种方法提高了可复制性,并简化了液体活检的分析.
- 工作流程作为 MALDI MS 在瘤学的常规临床实践中采用的基础.
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