Chemometrics play a critical role in biosensors-based detection, analysis, and diagnosis. Nowadays, as a branch of artificial intelligence (AI), machine learning (ML) have achieved impressive advances. However, novel advanced ML methods, especially deep learning, which is famous for image analysis, facial recognition, and speech recognition, has remained relatively elusive to the biosensor community. Herein, how ML can be beneficial to biosensors is systematically discussed. The advantages and drawbacks of most popular ML algorithms are summarized on the basis of sensing data analysis. Specially, deep learning methods such as convolutional neural network (CNN) and recurrent neural network (RNN) are emphasized. Diverse ML-assisted electrochemical biosensors, wearable electronics, SERS and other spectra-based biosensors, fluorescence biosensors and colorimetric biosensors are comprehensively discussed. Furthermore, biosensor networks and multibiosensor data fusion are introduced. This review will nicely bridge ML with biosensors, and greatly expand chemometrics for detection, analysis, and diagnosis.

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通过机器学习推进生物传感器的发展

化学计量学在基于生物传感器的检测，分析和诊断中起着至关重要的作用。如今，作为人工智能（AI）的一个分支，机器学习（ML）取得了令人瞩目的进步。但是，新颖的高级ML方法（尤其是深度学习）在图像传感器，面部识别和语音识别方面颇有名气，但对于生物传感器社区而言仍然相对难以捉摸。在本文中，系统地讨论了ML如何对生物传感器有益。在传感数据分析的基础上，总结了最流行的机器学习算法的优缺点。特别强调了深度学习方法，例如卷积神经网络（CNN）和递归神经网络（RNN）。多样的ML辅助电化学生物传感器，可穿戴电子设备，SERS和其他基于光谱的生物传感器，全面讨论了荧光生物传感器和比色生物传感器。此外，介绍了生物传感器网络和多生物传感器数据融合。这篇评论将很好地将ML与生物传感器联系起来，并极大地扩展了化学计量学的检测，分析和诊断能力。