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Cited by 768

The DOI matches its Crossref record; 147 of the 147 references it cites were found in a registry or an index.

Advancing Biosensors with Machine Learning

Cui, Feiyun, Yue, Yun, Zhang, Yi, Zhang, Ziming, Zhou, H. Susan·2020·ACS Sensors
DOI 10.1021/acssensors.0c01424
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Abstract

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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Metadata

Authors
Cui, F., Yue, Y., Zhang, Y., Zhang, Z., & Zhou, H. S.
Venue
ACS Sensors
Publisher
American Chemical Society (ACS)
Published
2020-11-13
Volume
5
Issue
11
Pages
3346-3364
ISSN
2379-3694 (ISSN-L)
DOI
10.1021/acssensors.0c01424
OpenAlex
W3100709371
Cited by
768
References
147
FWCI
23.80
Type
journal article
Language
en
Open access
closed
Licence
Funders
National Science Foundation (CBET-1805514)

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CoChat. (2026). Citation check: Advancing Biosensors with Machine Learning. https://app.cochat.ai/papers/10.1021/acssensors.0c01424
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Cui, F., Yue, Y., Zhang, Y., Zhang, Z., & Zhou, H. S. (2020). Advancing Biosensors with Machine Learning. ACS Sensors. https://doi.org/10.1021/acssensors.0c01424

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