用于SERS生物定量的可解释深度学习框架
Explainable Deep Learning Framework for SERS Bio-quantification
AI总结：
提出一种新型SERS生物定量框架，结合去噪自编码器、CNN和CRIME可解释性方法，实现血清素高效定量并揭示预测上下文，为非靶向生物标志物发现提供新途径。
摘要：
表面增强拉曼光谱（SERS）是一种潜在的分析物快速且低成本的定量方法，可结合深度学习来发现生物标志物与疾病的关系。本研究旨在通过一种包含光谱处理、分析物定量和模型可解释性的新型SERS生物定量框架，解决当前SERS面临的挑战。为此，以尿液介质中的血清素定量为模型任务，使用葫芦[8]脲化学间隔物测量了微摩尔范围内的682条SERS光谱。利用去噪自编码器进行光谱增强，并利用卷积神经网络（CNN）和视觉Transformer进行生物标志物定量。最后，开发了一种新型上下文代表性可解释模型解释（CRIME）方法，以满足当前SERS混合物分析可解释性的需求。使用带有三参数逻辑输出层的卷积神经网络分析的去噪光谱中，血清素定量最为高效（平均绝对误差 = 0.15 μM，平均百分比误差 = 4.67%）。随后，CRIME方法揭示CNN模型呈现六种预测上下文，其中三种与血清素相关。鉴于SERS测量的快速与低成本特性，以及从CRIME上下文中识别生物标志物的潜力，所提框架可解锁一种新型、非靶向的生成生物标志物发现假设的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
