Predicting Analyte Concentrations from Electrochemical Aptasensor Signals Using LSTM Recurrent Networks

Predicting Analyte Concentrations from Electrochemical Aptasensor Signals Using LSTM Recurrent Networks

Nanomaterial-based aptasensors are useful devices capable of detecting small biological species. Determining suitable signal processing methods can improve the identification and quantification of target analytes detected by the biosensor and consequently improve the biosensor’s performance. In this...

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Bibliographic Details
Main Authors: Fatemeh Esmaeili, Erica Cassie, Hong Phan T. Nguyen, Natalie O. V. Plank, Charles P. Unsworth, Alan Wang
Format: Article
Language:English
Published: MDPI AG 2022-10-01
Series:Bioengineering
Subjects:
data augmentation
multi-class classifiers
classification
deep learning
long short-term memory neural networks
unidirectional LSTM
Online Access:https://www.mdpi.com/2306-5354/9/10/529

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https://www.mdpi.com/2306-5354/9/10/529

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