ArticlePloS one2019
MALDI-TOF mass spectrometry on intact bacteria combined with a refined analysis framework allows accurate classification of MSSA and MRSA.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 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
- Application of machine learning with MALDI-TOF MS for rapid differentiation between methicillin-susceptible and methicillin-resistant Staphylococcus aureus.PLoS computational biology · 2026Article
- Differences and fingerprints of ESBL-producing E. coli from chicken faeces.Scientific reports · 2025Article
- Discrimination of Klebsiella pneumoniae and Klebsiella quasipneumoniae by MALDI-TOF Mass Spectrometry Coupled With Machine Learning.MicrobiologyOpen · 2025Article
- Application of matrix-assisted laser desorption/ionization time-of flight mass spectrometry in clinical testing and diagnosis.Frontiers in cellular and infection microbiology · 2025Review
- Intact cell lipidomics using the Bruker MBT lipid Xtract assay allows the rapid detection of glycosyl-inositol-phospho-ceramides fromMolecular omics · 2024Article
- Proteomic assay for rapid characterisation of Staphylococcus aureus antimicrobial resistance mechanisms directly from blood cultures.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2024Article
- Distinguishing methicillin-resistant Staphylococcus aureus from methicillin-sensitive strains by combining FeMikrochimica acta · 2024Article
- A magnetic nanozyme platform for bacterial colorimetric detection and chemodynamic/photothermal synergistic antibacterial therapy.Mikrochimica acta · 2024Article
- Multimodal learning in clinical proteomics: enhancing antimicrobial resistance prediction models with chemical information.Bioinformatics (Oxford, England) · 2023Article
- Towards Accurate Identification of Antibiotic-Resistant Pathogens through the Ensemble of Multiple Preprocessing Methods Based on MALDI-TOF Spectra.International journal of molecular sciences · 2023Article
- Discrimination of Escherichia coli, Shigella flexneri, and Shigella sonnei using lipid profiling by MALDI-TOF mass spectrometry paired with machine learning.MicrobiologyOpen · 2022Article
- Discrimination of Methicillin-resistantPathogens (Basel, Switzerland) · 2022Article
- Large-Scale Samples Based Rapid Detection of Ciprofloxacin Resistance inFrontiers in microbiology · 2022Article
- Mass spectrometry and machine learning for the accurate diagnosis of benzylpenicillin and multidrug resistance of Staphylococcus aureus in bovine mastitis.PLoS computational biology · 2021Article
- A large-scale investigation and identification of methicillin-resistant Staphylococcus aureus based on peaks binning of matrix-assisted laser desorption ionization-time of flight MS spectra.Briefings in bioinformatics · 2021Article
- Rapid Methods for Antimicrobial Resistance Diagnostics.Antibiotics (Basel, Switzerland) · 2021Review
- Combination of MALDI-TOF Mass Spectrometry and Machine Learning for Rapid Antimicrobial Resistance Screening: The Case ofFrontiers in microbiology · 2021Article
- Bacterial species identification using MALDI-TOF mass spectrometry and machine learning techniques: A large-scale benchmarking study.Computational and structural biotechnology journal · 2021Article
- MDRSA: A Web Based-Tool for Rapid Identification of Multidrug ResistantFrontiers in microbiology · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Fast and reliable detection coupled with accurate data-processing and analysis of antibiotic-resistant bacteria is essential in clinical settings. In this study, we use MALDI-TOF on intact cells combined with a refined analysis framework to demonstrate discrimination between methicillin-susceptible (MSSA) and methicillin-resistant (MRSA) Staphylococcus aureus. By combining supervised and unsupervised machine learning methods, we firstly show that the mass spectroscopy data contains strong signal for the clustering of MSSA and MRSA. Then we concentrate on applying supervised learning to extract and verify the important features. A new workflow is proposed that allows for extracting a fixed set of reference peaks so that any new data can be aligned to it and hence consistent feature matrices can be obtained. Also note that by doing so we are able to examine the robustness of the important features that have been found. We also show that appropriate size of the benchmark data, appropriate alignment of the testing data and use of an optimal set of features via feature selection results in prediction accuracy over 90%. In summary, as proof-of-principle, our integrated experimental and bioinformatics study suggests a novel intact cell MALDI-TOF to be of great promise for fast and reliable detection of MRSA strains.
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Registered trials
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