同时对多种代谢物的定量分析,使用无标签的表面增强拉曼光谱和可解释的深度学习
Xianli Tian1, Peng Wang2, Guoqiang Fang3
1School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China; Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, China.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
|November 7, 2024
概括
这项研究提出了一种结合表面增强拉曼光谱 (SERS) 和深度学习的新方法,用于同时检测代谢物. 该技术准确量化了多种代谢物,为临床诊断提供了一个敏感的工具.
科学领域:
- 分析化学 分析化学
- 生物标志物发现发现
- 计算生物学 计算生物学
背景情况:
- 代谢物是生理和病理状态的关键生物标志物,有助于监测疾病进展和早期检测.
- 现有的代谢物检测分析技术的灵敏度,成本效益或同时分析的速度可能受到限制.
研究的目的:
- 开发和验证一种先进的分析技术,用于同时定量检测多种代谢物.
- 将无标签的表面增强拉曼光谱 (SERS) 与深度学习和SHAP集成,以提高可解释性.
- 建立一种用于临床诊断的敏感,经济有效和快速的代谢物分析方法.
主要方法:
- 使用三相界面自组装方法制造单层银纳米粒子SERS基板.
- 开发一个定制的深度神经网络模型,采用多通道特征提取,用于光谱数据分析.
- 应用SHAP (SHapley增量解释) 进行深度学习模型预测的视觉解释性分析.
主要成果:
- 同时检测和定量分析尿酸,黄素,低黄素和肌素,准确度高 (R2值在0.940至0.977).
- 证明了该方法的可扩展性,随着同时目标数量的增加,性能保持一致.
- 从混合溶液中的目标代谢物中成功捕获复杂的光谱信息,使用制造的SERS基板.
结论:
- 集成的SERS和深度学习方法为多代谢物分析提供了一个灵敏,经济高效和快速的平台.
- 该方法显示了促进临床诊断和通过精确的代谢物概况来实现个性化医疗的巨大潜力.
- SHAP分析为深度学习模型的预测逻辑提供了有价值的见解,增强了信任和理解.
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