Deep Transfer Learning Enables Robust Prediction of Antimicrobial Resistance for Novel Antibiotics
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 traini...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
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| Series: | Antibiotics |
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| Online Access: | https://www.mdpi.com/2079-6382/11/11/1611 |
| _version_ | 1827645140638892032 |
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| author | Yunxiao Ren Trinad Chakraborty Swapnil Doijad Linda Falgenhauer Jane Falgenhauer Alexander Goesmann Oliver Schwengers Dominik Heider |
| author_facet | Yunxiao Ren Trinad Chakraborty Swapnil Doijad Linda Falgenhauer Jane Falgenhauer Alexander Goesmann Oliver Schwengers Dominik Heider |
| author_sort | Yunxiao Ren |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T18:31:14Z |
| format | Article |
| id | doaj.art-666ed2775c954218b3f9a53dce7f3b67 |
| institution | Directory Open Access Journal |
| issn | 2079-6382 |
| language | English |
| last_indexed | 2024-03-09T18:31:14Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Antibiotics |
| spelling | doaj.art-666ed2775c954218b3f9a53dce7f3b672023-11-24T07:30:37ZengMDPI AGAntibiotics2079-63822022-11-011111161110.3390/antibiotics11111611Deep Transfer Learning Enables Robust Prediction of Antimicrobial Resistance for Novel AntibioticsYunxiao Ren0Trinad Chakraborty1Swapnil Doijad2Linda Falgenhauer3Jane Falgenhauer4Alexander Goesmann5Oliver Schwengers6Dominik Heider7Department of Data Science in Biomedicine, Faculty of Mathematics and Computer Science, Philipps-University of Marburg, 35032 Marburg, GermanyInstitute of Medical Microbiology, Justus Liebig University Giessen, 35392 Giessen, GermanyInstitute of Medical Microbiology, Justus Liebig University Giessen, 35392 Giessen, GermanyGerman Center for Infection Research, Partner Site Giessen-Marburg-Langen, 35392 Giessen, GermanyInstitute of Medical Microbiology, Justus Liebig University Giessen, 35392 Giessen, GermanyGerman Center for Infection Research, Partner Site Giessen-Marburg-Langen, 35392 Giessen, GermanyGerman Center for Infection Research, Partner Site Giessen-Marburg-Langen, 35392 Giessen, GermanyDepartment of Data Science in Biomedicine, Faculty of Mathematics and Computer Science, Philipps-University of Marburg, 35032 Marburg, GermanyAntimicrobial 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.https://www.mdpi.com/2079-6382/11/11/1611transfer learningantimicrobial resistancesmall data with imbalanced label |
| spellingShingle | Yunxiao Ren Trinad Chakraborty Swapnil Doijad Linda Falgenhauer Jane Falgenhauer Alexander Goesmann Oliver Schwengers Dominik Heider Deep Transfer Learning Enables Robust Prediction of Antimicrobial Resistance for Novel Antibiotics Antibiotics transfer learning antimicrobial resistance small data with imbalanced label |
| title | Deep Transfer Learning Enables Robust Prediction of Antimicrobial Resistance for Novel Antibiotics |
| title_full | Deep Transfer Learning Enables Robust Prediction of Antimicrobial Resistance for Novel Antibiotics |
| title_fullStr | Deep Transfer Learning Enables Robust Prediction of Antimicrobial Resistance for Novel Antibiotics |
| title_full_unstemmed | Deep Transfer Learning Enables Robust Prediction of Antimicrobial Resistance for Novel Antibiotics |
| title_short | Deep Transfer Learning Enables Robust Prediction of Antimicrobial Resistance for Novel Antibiotics |
| title_sort | deep transfer learning enables robust prediction of antimicrobial resistance for novel antibiotics |
| topic | transfer learning antimicrobial resistance small data with imbalanced label |
| url | https://www.mdpi.com/2079-6382/11/11/1611 |
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