ArticleAntibiotics (Basel, Switzerland)2022
Deep Transfer Learning Enables Robust Prediction of Antimicrobial Resistance for Novel Antibiotics.
Article in Antibiotics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning Approaches for Microorganism Identification, Virulence Assessment, and Antimicrobial Susceptibility Evaluation Using DNA Sequencing Methods: A Systematic Review.Molecular biotechnology · 2025Pooled it
- Construction and validation of a phenotypic prediction model for bacterial gentamicin resistance using deep learning with gene sequences.Microbiology spectrum · 2026Article
- Negative-Ion Mode MALDI-TOF MS Combined with Machine Learning for the Rapid Identification of Colistin-ResistantACS omega · 2026Article
- Artificial Intelligence in Bacteriophage Science: A Comprehensive Narrative Review of Applications, Challenges, and Translational Opportunities.Antibiotics (Basel, Switzerland) · 2026Review
- Vancomycin resistance in gram-positive infections: evolutionary strategies of survival.Archives of microbiology · 2026Review
- Artificial Intelligence in the Diagnosis and Management of Pulmonary Tuberculosis: A Review of Current Applications and Future Perspectives.Therapeutics and clinical risk management · 2026Review
- aiGeneR 3.0: an enhanced deep network model for resistant strain identification and multi-drug resistance prediction inFrontiers in genetics · 2025Article
- NeuralBeds: Neural embeddings for efficient DNA data compression and optimized similarity search.Computational and structural biotechnology journal · 2024Article
- Review
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Artificial intelligence in predicting pathogenic microorganisms' antimicrobial resistance: challenges, progress, and prospects.Frontiers in cellular and infection microbiology · 2024Review
- MSDeepAMR: antimicrobial resistance prediction based on deep neural networks and transfer learning.Frontiers in microbiology · 2024Article
- Artificial Intelligence for Antimicrobial Resistance Prediction: Challenges and Opportunities towards Practical Implementation.Antibiotics (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
8 authors.
Funding
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.
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Registered trials
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