Imperial College London
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Professor Pantelis Georgiou

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor of Biomedical Electronics
 
 
 
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Contact

 

+44 (0)20 7594 6326pantelis Website

 
 
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Location

 

902Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Mao:2023:10.1109/JBHI.2023.3257727,
author = {Mao, Y and Miglietta, L and Kreitmann, L and Moser, N and Georgiou, P and Holmes, A and Rodriguez, Manzano J},
doi = {10.1109/JBHI.2023.3257727},
journal = {IEEE Journal of Biomedical and Health Informatics},
pages = {3093--3103},
title = {Deep domain adaptation enhances Amplification Curve Analysis for single-channel multiplexing in real-time PCR},
url = {http://dx.doi.org/10.1109/JBHI.2023.3257727},
volume = {27},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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 cli
AU  - Mao,Y
AU  - Miglietta,L
AU  - Kreitmann,L
AU  - Moser,N
AU  - Georgiou,P
AU  - Holmes,A
AU  - Rodriguez,Manzano J
DO  - 10.1109/JBHI.2023.3257727
EP  - 3103
PY  - 2023///
SN  - 2168-2208
SP  - 3093
TI  - Deep domain adaptation enhances Amplification Curve Analysis for single-channel multiplexing in real-time PCR
T2  - IEEE Journal of Biomedical and Health Informatics
UR  - http://dx.doi.org/10.1109/JBHI.2023.3257727
UR  - https://ieeexplore.ieee.org/document/10070782
UR  - http://hdl.handle.net/10044/1/103518
VL  - 27
ER  -
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