ArticleFrontiers in microbiology2024
MSDeepAMR: antimicrobial resistance prediction based on deep neural networks and transfer learning.
Article in Frontiers in microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Computational paradigms for antimicrobial resistance prediction: integrating multi-omics, structural modeling, and foundation artificial intelligence systems.Briefings in bioinformatics · 2026Pooled it
- A systematic review of machine learning on clinical MALDI-TOF MS.Briefings in bioinformatics · 2026Pooled it
- Negative-Ion Mode MALDI-TOF MS Combined with Machine Learning for the Rapid Identification of Colistin-ResistantACS omega · 2026Article
- Rapid identification of carbapenem-resistant Acinetobacter baumannii based on MALDI-TOF mass spectrometry and machine learning.BMC microbiology · 2026Article
- Benchmarking deep learning architectures for MALDI-TOF mass spectrometry in infectious disease diagnostics and vector-borne disease surveillance.NPJ digital medicine · 2026Article
- Improving the Precision of Etiological Diagnosis in Bacterial Infections Using Molecular Technologies: A Comparative Analysis of Platforms, AI Integration, and Point-of-Care Deployment.International journal of molecular sciences · 2026Review
- Review
- ReShuffle-MS: Region-Guided Data Augmentation Improves Artificial Intelligence-Based Resistance Prediction inMicroorganisms · 2026Article
- Artificial Intelligence for Antimicrobial Resistance Detection and Prediction inInfection and drug resistance · 2026Review
- Advances in deep learning for antimicrobial research.Frontiers in microbiology · 2026Review
- StaLAENet: A stacked LSTM-nested deep-autoencoder network for identification of antimicrobial resistance of nosocomial pathogens.Journal of biosciences · 2026Article
- Microbial Primer: Artificial intelligence for microbiologists.Microbiology (Reading, England) · 2025Review
- Unlocking antimicrobial resistance with multiomics and machine learning.Trends in microbiology · 2025Article
- Machine Learning for Multi-Target Drug Discovery: Challenges and Opportunities in Systems Pharmacology.Pharmaceutics · 2025Review
- The Gut Microbiome and Colistin Resistance: A Hidden Driver of Antimicrobial Failure.International journal of molecular sciences · 2025Review
- Proteomic Insights into Bacterial Responses to Antibiotics: A Narrative Review.International journal of molecular sciences · 2025Review
- How AI can help us beat AMR.npj antimicrobials and resistance · 2025Review
- Insight into the Relationships Between Chemical, Protein and Functional Variables in the PBP/GOBP Family in Moths Based on Machine Learning.International journal of molecular sciences · 2025Article
- Integrating Machine Learning with MALDI-TOF Mass Spectrometry for Rapid and Accurate Antimicrobial Resistance Detection in Clinical Pathogens.International journal of molecular sciences · 2025Article
- Integrating sequencing methods with machine learning for antimicrobial susceptibility testing in pediatric infections: current advances and future insights.Frontiers in microbiology · 2025Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Antimicrobial resistance (AMR) is a global health problem that requires early and effective treatments to prevent the indiscriminate use of antimicrobial drugs and the outcome of infections. Mass Spectrometry (MS), and more particularly MALDI-TOF, have been widely adopted by routine clinical microbiology laboratories to identify bacterial species and detect AMR. The analysis of AMR with deep learning is still recent, and most models depend on filters and preprocessing techniques manually applied on spectra. Methods: This study propose a deep neural network, MSDeepAMR, to learn from raw mass spectra to predict AMR. MSDeepAMR model was implemented for Escherichia coli, Klebsiella pneumoniae, and Staphylococcus aureus under different antibiotic resistance profiles. Additionally, a transfer learning test was performed to study the benefits of adapting the previously trained models to external data. Results: MSDeepAMR models showed a good classification performance to detect antibiotic resistance. The AUROC of the model was above 0.83 in most cases studied, improving the results of previous investigations by over 10%. The adapted models improved the AUROC by up to 20% when compared to a model trained only with external data. Discussion: This study demonstrate the potential of the MSDeepAMR model to predict antibiotic resistance and their use on external MS data. This allow the extrapolation of the MSDeepAMR model to de used in different laboratories that need to study AMR and do not have the capacity for an extensive sample collection.
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