LCMS-Net:深度学习原始高分辨率质谱数据应用到法医死亡原因查
Lisa M Menacher1, Liam J Ward2,3, Fredrik Heintz1,4
1Department of Computer and Information Science, Linköping University, 581 83 Linköping, Sweden.
Analytical chemistry
|February 27, 2026
概括
深度学习模型LCMS-Net自动化了从原始液体染色学高分辨率质谱数据的非目标代谢学分析. 这种方法改善了代谢物检测,减少了批量效应,提高了研究中的可重现性.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 分析化学 分析化学
背景情况:
- 使用液体染色学高分辨率质谱学 (LC-HRMS) 的非向代谢学涉及复杂,耗时的预处理.
- 现有的工作流程往往缺乏可重现性,可能会错过关键的代谢物数据.
- 对于数据分析,通常需要大量的领域专业知识.
研究的目的:
- 引入LCMS-Net,这是一个端到端的深度学习模型,用于自动化LC-HRMS数据分析.
- 在当前的预加工方法中,解决时间,可重复性和代谢物检测方面的挑战.
- 为代谢学研究提供更有效,更强大的计算工具.
主要方法:
- 开发了LCMS-Net,这是一个直接运行在原始LC-HRMS数据上的深度学习模型.
- 在模型架构中明确建模LC-HRMS数据的空间属性.
- 验证了死因查模型和结肠癌检测案例研究.
主要成果:
- 与OPLS-DA相比,LCMS-Net在死因查方面取得了9%的F1评分改善.
- 与DeepMSProfiler相比,LCMS-Net在结肠癌检测方面表现出1.8%的F1评分改善.
- 该模型显著减少了批量效应,不同仪器的性能差异仅为3%.
- 在计算上,LCMS-Net比其他端到端深度学习方法更高效,更快,更简单,不需要预训练.
结论:
- 对于LC-HRMS数据分析,LCMS-Net提供了一个完全自动化的,可重复的,高效的工作流.
- 该模型增强了代谢物检测,并最大限度地减少了批量效应,提高了代谢学研究的可靠性.
- LCMS-Net代表了计算代谢学方面的重大进步,适用于各种生物和临床研究领域.
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