用深度学习提升拉曼光谱定量分析精度并解释预测依据。
Explainable Deep Learning Framework for SERS Bio-quantification
- 用去噪自编码器增强光谱，结合CNN和视觉变换器定量生物标志物。
- 对尿液中5-羟色胺的定量误差仅0.15μM（均方误差），准确率超95%。
- 提出CRIME解释方法，揭示模型决策上下文，助力无目标生物标志物发现。
表面增强拉曼光谱（SERS）是一种快速且低成本的分析物定量方法，可与深度学习结合以揭示生物标志物与疾病的关系。本研究提出一种新型SERS生物定量框架，涵盖光谱处理、分析物定量及模型可解释性。以尿液中5-羟色胺定量为范例任务，使用杯[8]脲化学间隔子采集了682组微摩尔浓度范围的SERS光谱。采用去噪自编码器进行光谱增强，卷积神经网络（CNN）和视觉变换器用于生物标志物定量。最后开发了一种新的上下文代表性可解释模型解释方法（CRIME），满足当前混合物分析的可解释性需求。在经去噪处理的光谱上，基于三参数逻辑输出层的CNN模型表现最优，平均绝对误差为0.15 μM，平均百分比误差为4.67%。CRIME方法揭示了该模型存在六个预测上下文，其中三个与5-羟色胺相关。所提框架利用SERS测量的快速性与低成本特性，有望开启一种全新的无目标生物标志物发现途径。
原文摘要 · Abstract (English)
Surface-enhanced Raman spectroscopy (SERS) is a potential fast and inexpensive method of analyte quantification, which can be combined with deep learning to discover biomarker-disease relationships. This study aims to address present challenges of SERS through a novel SERS bio-quantification framework, including spectral processing, analyte quantification, and model explainability. To this end,serotonin quantification in urine media was assessed as a model task with 682 SERS spectra measured in a micromolar range using cucurbit[8]uril chemical spacers. A denoising autoencoder was utilized for spectral enhancement, and convolutional neural networks (CNN) and vision transformers were utilized for biomarker quantification. Lastly, a novel context representative interpretable model explanations (CRIME) method was developed to suit the current needs of SERS mixture analysis explainability. Serotonin quantification was most efficient in denoised spectra analysed using a convolutional neural network with a three-parameter logistic output layer (mean absolute error = 0.15 μM, mean percentage error = 4.67%). Subsequently, the CRIME method revealed the CNN model to present six prediction contexts, of which three were associated with serotonin. The proposed framework could unlock a novel, untargeted hypothesis generating method of biomarker discovery considering the rapid and inexpensive nature of SERS measurements, and the potential to identify biomarkers from CRIME contexts.
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