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Deep Transfer Learning Enables Robust Prediction of Antimicrobial Resistance for Novel Antibiotics

Authors :
Yunxiao Ren
Trinad Chakraborty
Swapnil Doijad
Linda Falgenhauer
Jane Falgenhauer
Alexander Goesmann
Oliver Schwengers
Dominik Heider
Source :
Antibiotics, Vol 11, Iss 11, p 1611 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

Antimicrobial resistance (AMR) has become one of the serious global health problems, threatening the effective treatment of a growing number of infections. Machine learning and deep learning show great potential in rapid and accurate AMR predictions. However, a large number of samples for the training of these models is essential. In particular, for novel antibiotics, limited training samples and data imbalance hinder the models’ generalization performance and overall accuracy. We propose a deep transfer learning model that can improve model performance for AMR prediction on small, imbalanced datasets. As our approach relies on transfer learning and secondary mutations, it is also applicable to novel antibiotics and emerging resistances in the future and enables quick diagnostics and personalized treatments.

Details

Language :
English
ISSN :
20796382
Volume :
11
Issue :
11
Database :
Directory of Open Access Journals
Journal :
Antibiotics
Publication Type :
Academic Journal
Accession number :
edsdoj.666ed2775c954218b3f9a53dce7f3b67
Document Type :
article
Full Text :
https://doi.org/10.3390/antibiotics11111611