Surface-enhanced Raman spectroscopy (SERS) has wide diagnostic applications due to narrow spectral features that allow multiplex analysis. We have previously developed a multiplexed, SERS-based nanosensor for micro-RNA (miRNA) detection called the inverse molecular sentinel (iMS). Machine learning (ML) algorithms have been increasingly adopted for spectral analysis due to their ability to discover underlying patterns and relationships within large and complex data sets. However, the high dimensionality of SERS data poses a challenge for traditional ML techniques, which can be prone to overfitting and poor generalization. Non-negative matrix factorization (NMF) reduces the dimensionality of SERS data while preserving information content. In this paper, we compared the performance of ML methods including convolutional neural network (CNN), support vector regression, and extreme gradient boosting combined with and without NMF for spectral unmixing of four-way multiplexed SERS spectra from iMS assays used for miRNA detection. CNN achieved high accuracy in spectral unmixing. Incorporating NMF before CNN drastically decreased memory and training demands without sacrificing model performance on SERS spectral unmixing. Additionally, models were interpreted using gradient class activation maps and partial dependency plots to understand predictions. These models were used to analyze clinical SERS data from single-plexed iMS in RNA extracted from 17 endoscopic tissue biopsies. CNN and CNN-NMF, trained on multiplexed data, performed most accurately with RMSElabel = 0.101 and 9.68 × 10-2, respectively. We demonstrated that CNN-based ML shows great promise in spectral unmixing of multiplexed SERS spectra, and the effect of dimensionality reduction on performance and training speed.

中文翻译：

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基于表面增强拉曼光谱的 Micro-RNA 生物标志物检测，用于生物医学诊断，使用可解释的机器学习算法的比较研究。

表面增强拉曼光谱 (SERS) 具有允许多重分析的窄光谱特征，因此具有广泛的诊断应用。我们之前开发了一种基于 SERS 的多重纳米传感器，用于检测微小 RNA (miRNA)，称为反向分子哨兵 (iMS)。机器学习 (ML) 算法由于能够发现大型复杂数据集中的潜在模式和关系，因此越来越多地用于频谱分析。然而，SERS 数据的高维性给传统的机器学习技术带来了挑战，传统的机器学习技术容易出现过度拟合和泛化能力差的情况。非负矩阵分解 (NMF) 降低了 SERS 数据的维数，同时保留了信息内容。在本文中，我们比较了 ML 方法的性能，包括卷积神经网络 (CNN)、支持向量回归和极端梯度增强，结合和不结合 NMF，对用于 miRNA 检测的 iMS 测定的四路多重 SERS 光谱进行光谱分离。CNN 在光谱分解方面实现了高精度。在 CNN 之前结合 NMF 大大降低了内存和训练需求，而不会牺牲 SERS 光谱分解的模型性能。此外，还使用梯度类激活图和部分依赖图来解释模型以理解预测。这些模型用于分析从 17 个内窥镜组织活检中提取的 RNA 中的单重 iMS 的临床 SERS 数据。CNN 和 CNN-NMF 在多路复用数据上进行训练，分别在 RMSElabel = 0.101 和 9.68 × 10-2 时表现最准确。我们证明了基于 CNN 的 ML 在多重 SERS 光谱的光谱分解以及降维对性能和训练速度的影响方面显示出巨大的前景。