ArticleScientific reports2024
Multi-label classification to predict antibiotic resistance from raw clinical MALDI-TOF mass spectrometry data.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of machine learning on clinical MALDI-TOF MS.Briefings in bioinformatics · 2026Pooled it
- AI-assisted MALDI-TOF MS for identifying carbapenem resistance in clinicalEmerging microbes & infections · 2026Article
- Enhanced classification and identification of bacterial and viral microorganisms by integration of MALDI-TOF mass spectrometry with artificial intelligence.Scientific reports · 2026Article
- Recent advancements in artificial intelligence applications for the mitigation of antimicrobial resistance: challenges and opportunities.JAC-antimicrobial resistance · 2026Review
- Rapid Multiplex Antimicrobial Resistance Profiling and Bacterial Identification by LAP-MALDI Mass Spectrometry Biotyping.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Rapid identification of carbapenem-resistant Acinetobacter baumannii based on MALDI-TOF mass spectrometry and machine learning.BMC microbiology · 2026Article
- Toward Robust Machine Learning Models for MALDI-TOF MS: Novel Approaches forJournal of proteome research · 2026Article
- ReShuffle-MS: Region-Guided Data Augmentation Improves Artificial Intelligence-Based Resistance Prediction inMicroorganisms · 2026Article
- AI-powered rapid detection of multidrug-resistantBioinformatics advances · 2026Article
- Artificial Intelligence for Antimicrobial Resistance Detection and Prediction inInfection and drug resistance · 2026Review
- Biochip-simulated genotype signals enable accurate and interpretable AMR prediction via machine learning.Frontiers in medicine · 2026Article
- Spectral Precision: Recent Advances in Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry for Pathogen Detection and Resistance Profiling.Microorganisms · 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
- Mapping the therapeutic versatility of WHO essential medicines: a systematic analysis of off-label indications.Therapeutic advances in drug safety · 2025Article
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Authors and funding
4 authors.
Funding
Abstract
Antimicrobial resistance (AMR) poses a significant global health challenge, necessitating advanced predictive models to support clinical decision-making. In this study, we explore multi-label classification as a novel approach to predict antibiotic resistance across four clinically relevant bacteria: E. coli, S. aureus, K. pneumoniae, and P. aeruginosa. Using multiple datasets from the DRIAMS repository, we evaluated the performance of four algorithms - Multi-Layer Perceptron, Support Vector Classifier, Random Forest, and Extreme Gradient Boosting - under both single-label and multi-label frameworks. Our results demonstrate that the multi-label approach delivers competitive performance compared to traditional single-label models, with no statistically significant differences in most cases. The multi-label framework naturally captures the complex, interconnected nature of AMR data, reflecting real-world scenarios more accurately. We further validated the models on external datasets (DRIAMS B and C), confirming their generalizability and robustness. Additionally, we investigated the impact of oversampling techniques and provided a reproducible methodology for handling MALDI-TOF data, ensuring scalability for future studies. These findings underscore the potential of multi-label classification to enhance predictive accuracy in AMR research, offering valuable insights for developing diagnostic tools and guiding clinical interventions.
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Registered trials
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