Journal article

Authors list: Ren, Yunxiao; Chakraborty, Trinad; Doijad, Swapnil; Falgenhauer, Linda; Falgenhauer, Jane; Goesmann, Alexander; Schwengers, Oliver; Heider, Dominik

Publication year: 2022

Journal: Antibiotics

Volume number: 11

Issue number: 11

ISSN: 2079-6382

Open access status: Gold

DOI Link: https://doi.org/10.3390/antibiotics11111611

Publisher: MDPI

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.

Harvard Citation style: Ren, Y., Chakraborty, T., Doijad, S., Falgenhauer, L., Falgenhauer, J., Goesmann, A., et al. (2022) Deep Transfer Learning Enables Robust Prediction of Antimicrobial Resistance for Novel Antibiotics, Antibiotics, 11(11), Article 1611. https://doi.org/10.3390/antibiotics11111611

APA Citation style: Ren, Y., Chakraborty, T., Doijad, S., Falgenhauer, L., Falgenhauer, J., Goesmann, A., Schwengers, O., & Heider, D. (2022). Deep Transfer Learning Enables Robust Prediction of Antimicrobial Resistance for Novel Antibiotics. Antibiotics. 11(11), Article 1611. https://doi.org/10.3390/antibiotics11111611