ArticlePloS one2018
A new scheme for strain typing of methicillin-resistant Staphylococcus aureus on the basis of matrix-assisted laser desorption ionization time-of-flight mass spectrometry by using machine learning approach.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 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
- Toward Robust Machine Learning Models for MALDI-TOF MS: Novel Approaches forJournal of proteome research · 2026Article
- Accelerated detection ofMicrobiology spectrum · 2026Article
- AI-powered rapid detection of multidrug-resistantBioinformatics advances · 2026Article
- Artificial Intelligence in Bacterial Infections Control: A Scoping Review.Antibiotics (Basel, Switzerland) · 2025Review
- Integrating Machine Learning with MALDI-TOF Mass Spectrometry for Rapid and Accurate Antimicrobial Resistance Detection in Clinical Pathogens.International journal of molecular sciences · 2025Article
- MALDI-TOF MS in conjunction with machine learning: toward a new era for antimicrobial susceptibility testing.Frontiers in cellular and infection microbiology · 2025Review
- Multi-label classification to predict antibiotic resistance from raw clinical MALDI-TOF mass spectrometry data.Scientific reports · 2024Article
- An antimicrobial drug recommender system using MALDI-TOF MS and dual-branch neural networks.eLife · 2024Article
- Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- MSDeepAMR: antimicrobial resistance prediction based on deep neural networks and transfer learning.Frontiers in microbiology · 2024Article
- Data-Driven Two-Stage Framework for Identification and Characterization of Different Antibiotic-Resistant Escherichia coli Isolates Based on Mass Spectrometry Data.Microbiology spectrum · 2023Article
- Rapid identification of carbapenem-resistant Klebsiella pneumoniae based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry and an artificial neural network model.Journal of biomedical science · 2023Article
- Antibiotic Resistance Diagnosis in ESKAPE Pathogens-A Review on Proteomic Perspective.Diagnostics (Basel, Switzerland) · 2023Review
- Predicting microbe organisms using data of living micro forms of life and hybrid microbes classifier.PloS one · 2023Article
- A Facile machine learning multi-classification model for Streptococcus agalactiae clonal complexes.Annals of clinical microbiology and antimicrobials · 2022Article
- Performance of a Machine Learning-Based Methicillin Resistance ofMicroorganisms · 2022Article
- Emerging vancomycin-non susceptible coagulase negative Staphylococci associated with skin and soft tissue infections.Annals of clinical microbiology and antimicrobials · 2022Article
- Investigating Unfavorable Factors That Impede MALDI-TOF-Based AI in Predicting Antibiotic Resistance.Diagnostics (Basel, Switzerland) · 2022Article
- Identification of a clonal population of Aspergillus flavus by MALDI-TOF mass spectrometry using deep learning.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Methicillin-resistant Staphylococcus aureus (MRSA), one of the most important clinical pathogens, conducts an increasing number of morbidity and mortality in the world. Rapid and accurate strain typing of bacteria would facilitate epidemiological investigation and infection control in near real time. Matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry is a rapid and cost-effective tool for presumptive strain typing. To develop robust method for strain typing based on MALDI-TOF spectrum, machine learning (ML) is a promising algorithm for the construction of predictive model. In this study, a strategy of building templates of specific types was used to facilitate generating predictive models of methicillin-resistant Staphylococcus aureus (MRSA) strain typing through various ML methods. The strain types of the isolates were determined through multilocus sequence typing (MLST). The area under the receiver operating characteristic curve (AUC) and the predictive accuracy of the models were compared. ST5, ST59, and ST239 were the major MLST types, and ST45 was the minor type. For binary classification, the AUC values of various ML methods ranged from 0.76 to 0.99 for ST5, ST59, and ST239 types. In multiclass classification, the predictive accuracy of all generated models was more than 0.83. This study has demonstrated that ML methods can serve as a cost-effective and promising tool that provides preliminary strain typing information about major MRSA lineages on the basis of MALDI-TOF spectra.
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