Deep domain adaptation enhances Amplification Curve Analysis for single-channel multiplexing in real-time PCR
Author(s)
Type
Journal Article
Abstract
Data-driven approaches for molecular diagnostics are emerging as an alternative to perform an accurate and inexpensive multi-pathogen detection. A novel technique called Amplification Curve Analysis (ACA) has been recently developed by coupling machine learning and real-time Polymerase Chain Reaction (qPCR) to enable the simultaneous detection of multiple targets in a single reaction well. However, target classification purely relying on the amplification curve shapes currently faces several challenges, such as distribution discrepancies between different data sources of synthetic DNA and clinical samples (i.e., training vs testing). Optimisation of computational models is required to achieve higher performance of ACA classification in multiplex qPCR through the reduction of those discrepancies. Here, we proposed a novel transformer-based conditional domain adversarial network (T-CDAN) to eliminate data distribution differences between the source domain (synthetic DNA data) and the target domain (clinical isolate data). The labelled training data from the source domain and unlabelled testing data from the target domain are fed into the T-CDAN, which learns both domains' information simultaneously. After mapping the inputs into a domain-irrelevant space, T-CDAN removes the feature distribution differences and provides a clearer decision boundary for the classifier, resulting in a more accurate pathogen identification. Evaluation of 198 clinical isolates containing three types of carbapenem-resistant genes ( bla NDM , bla IMP and bla OXA-48 ) illustrates a curve-level accuracy of 93.1% and a sample-level accuracy of 97.0% using T-CDAN, showing an accuracy improvement of 20.9% and 4.9% respectively, compared with previous methods. This research emphasises the importance of deep domain adaptation to enable high-level multiplexing in a single qPCR reaction, providing a solid approach to extend qPCR instruments' capabilities without hardware modification in real-world clinical applications.
Date Issued
2023-03-15
Date Acceptance
2023-03-10
Citation
IEEE Journal of Biomedical and Health Informatics, 2023, 27 (6), pp.3093-3103
ISSN
2168-2208
Publisher
Institute of Electrical and Electronics Engineers
Start Page
3093
End Page
3103
Journal / Book Title
IEEE Journal of Biomedical and Health Informatics
Volume
27
Issue
6
Copyright Statement
© 2023 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
License URL
Identifier
https://ieeexplore.ieee.org/document/10070782
Publication Status
Published
Date Publish Online
2023-03-15