Biomedical Research Centre , Mills Breast Cancer Research Institute and Carle Foundation Hospital , Urbana , Illinois 61801 , United States.
Wolfram Research Inc. , Champaign , Illinois 61801 , United States.
ACS Sens. 2019 Oct 25;4(10):2730-2737. doi: 10.1021/acssensors.9b01227. Epub 2019 Oct 11.
Fluorescent array-based sensing is an emerging differential sensing platform for sensitive detection of analytes in a complex environment without involving a conventional "lock and key" type-specific interaction. These sensing techniques mainly rely on different optical pattern generation from a sensor array and their pattern recognition to differentiate analytes. Currently emerging, compelling pattern-recognition method, Machine Learning (ML), enables a machine to "learn" a pattern by training without having the recognition method explicitly programmed into it. Thus, ML has an enormous potential to analyze these sensing data better than widely used statistical pattern-recognition methods. Here, an array-based sensor using easy-to-synthesize carbon dots with varied surface functionality is reported, which can differentiate between eight different proteins at 100 nM concentration. The utility of using machine learning algorithms in pattern recognition of fluorescence signals from the array has also been demonstrated. In analyzing the array-based sensing data, Machine Learning algorithms like "Gradient-Boosted Trees" have achieved a 100% prediction efficiency compared to inferior-performing classical statistical method "Linear Discriminant Analysis".
荧光阵列传感是一种新兴的差分传感平台，用于在复杂环境中灵敏地检测分析物，而无需涉及传统的“锁钥”型特异性相互作用。这些传感技术主要依赖于传感器阵列产生的不同光学图案及其模式识别来区分分析物。目前新兴的、引人注目的模式识别方法机器学习（ML）使机器能够“学习”模式而无需对其进行显式编程。因此，ML 具有巨大的潜力，可以比广泛使用的统计模式识别方法更好地分析这些传感数据。在这里，报道了一种基于阵列的传感器，该传感器使用易于合成的具有不同表面功能的碳点，可以在 100 nM 浓度下区分八种不同的蛋白质。还证明了在对来自阵列的荧光信号进行模式识别时使用机器学习算法的实用性。在分析基于阵列的传感数据时，机器学习算法（如“梯度提升树”）与性能较差的经典统计方法“线性判别分析”相比，实现了 100%的预测效率。
