ArticleMicrobiology spectrum2022
Rapid Identification of Methicillin-Resistant Staphylococcus aureus Using MALDI-TOF MS and Machine Learning from over 20,000 Clinical Isolates.
Article in Microbiology spectrum, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 63 citations in OpenAlex.
- A systematic review of machine learning on clinical MALDI-TOF MS.Briefings in bioinformatics · 2026Pooled it
- Rapid Screening of Methicillin-ResistantAnalytical chemistry · 2025Trial
- Rapid Identification of Infections Directly from Isolates and Clinical Specimens with the MasSpec Pen Technology.bioRxiv : the preprint server for biology · 2026Article
- Application of machine learning with MALDI-TOF MS for rapid differentiation between methicillin-susceptible and methicillin-resistant Staphylococcus aureus.PLoS computational biology · 2026Article
- Multidrug Resistance and Adaptive Response to Silver and Gold Nanoparticles in Methicillin-ResistantPathogens (Basel, Switzerland) · 2026Article
- Integrating MALDI-TOF Mass Spectrometry and Machine Learning for Rapid and Clinically Relevant Differentiation of MRSA and MSSA.Pathogens (Basel, Switzerland) · 2026Article
- Accelerated detection ofMicrobiology spectrum · 2026Article
- Non-Targeted Screening Method for Detecting Temporal Shifts in Spectral Patterns of Methicillin-ResistantMicroorganisms · 2026Article
- Prediction of antimicrobial resistance from MALDI-TOF mass spectra using machine learning: a validation study.Journal of clinical microbiology · 2025Article
- A Review on the Current and Future State of Urinary Tract Infection Diagnostics.International journal of molecular sciences · 2025Review
- Combating Foodborne MRSA: Identification and Silver Nanoparticle-Based Antibacterial Strategies with Antibiotic Synergy and Resistance Evolution Assessment.Microorganisms · 2025Article
- MALDI-TOF MS Biomarkers for Methicillin-ResistantMetabolites · 2025Review
- Nutritional optimization for bioprocess production of staphyloxanthin from Staphylococcus aureus with response surface methodology: promising anticancer scaffold targeting EGFR inhibition.Microbial cell factories · 2025Article
- End-To-End Deep Learning Explains Antimicrobial Resistance in Peak-Picking-Free MALDI-MS Data.Analytical chemistry · 2025Article
- MALDI-TOF MS in conjunction with machine learning: toward a new era for antimicrobial susceptibility testing.Frontiers in cellular and infection microbiology · 2025Review
- Genomic characterization ofFrontiers in microbiology · 2025Article
- MALDI-TOF mass spectrometry discriminates drug-susceptible and -resistant strains in Mycobacterium abscessus.PloS one · 2025Article
- An antimicrobial drug recommender system using MALDI-TOF MS and dual-branch neural networks.eLife · 2024Article
- Pioneering Klebsiella Pneumoniae Antibiotic Resistance Prediction With Artificial Intelligence-Clinical Decision Support System-Enhanced Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry: Retrospective Study.Journal of medical Internet research · 2024Article
- Repurposing MALDI-TOF MS for effective antibiotic resistance screening in Staphylococcus epidermidis using machine learning.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rapidly identifying methicillin-resistant Staphylococcus aureus (MRSA) with high integration in the current workflow is critical in clinical practices. We proposed a matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS)-based machine learning model for rapid MRSA prediction. The model was evaluated on a prospective test and four external clinical sites. For the data set comprising 20,359 clinical isolates, the area under the receiver operating curve of the classification model was 0.78 to 0.88. These results were further interpreted using shapely additive explanations and presented using the pseudogel method. The important MRSA feature,
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.