Rong Guoguang, Xu Yankun, Sawan Mohamad
CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou 310030, China.
Biosensors (Basel). 2023 Aug 31;13(9):860. doi: 10.3390/bios13090860.
We describe a machine learning (ML) approach to processing the signals collected from a COVID-19 optical-based detector. Multilayer perceptron (MLP) and support vector machine (SVM) were used to process both the raw data and the feature engineering data, and high performance for the qualitative detection of the SARS-CoV-2 virus with concentration down to 1 TCID/mL was achieved. Valid detection experiments contained 486 negative and 108 positive samples, and control experiments, in which biosensors without antibody functionalization were used to detect SARS-CoV-2, contained 36 negative samples and 732 positive samples. The data distribution patterns of the valid and control detection dataset, based on T-distributed stochastic neighbor embedding (t-SNE), were used to study the distinguishability between positive and negative samples and explain the ML prediction performance. This work demonstrates that ML can be a generalized effective approach to process the signals and the datasets of biosensors dependent on resonant modes as biosensing mechanism.
我们描述了一种机器学习（ML）方法，用于处理从基于光学的COVID-19探测器收集的信号。使用多层感知器（MLP）和支持向量机（SVM）来处理原始数据和特征工程数据，并实现了对浓度低至1 TCID/mL的SARS-CoV-2病毒进行定性检测的高性能。有效检测实验包含486个阴性样本和108个阳性样本，而使用未进行抗体功能化的生物传感器检测SARS-CoV-2的对照实验包含36个阴性样本和732个阳性样本。基于T分布随机邻域嵌入（t-SNE）的有效和对照检测数据集的数据分布模式，用于研究阳性和阴性样本之间的可区分性，并解释ML预测性能。这项工作表明，ML可以作为一种通用的有效方法，来处理依赖于共振模式作为生物传感机制的生物传感器的信号和数据集。
