ArticleFrontiers in microbiology2022
Efficiently Predicting Vancomycin Resistance of
Article in Frontiers in microbiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
- A systematic review of machine learning on clinical MALDI-TOF MS.Briefings in bioinformatics · 2026Pooled it
- Artificial Intelligence and Bioengineering Approaches for Antimicrobial Resistance Prediction.Medicina (Kaunas, Lithuania) · 2026Review
- Negative-Ion Mode MALDI-TOF MS Combined with Machine Learning for the Rapid Identification of Colistin-ResistantACS omega · 2026Article
- Benchmarking deep learning architectures for MALDI-TOF mass spectrometry in infectious disease diagnostics and vector-borne disease surveillance.NPJ digital medicine · 2026Article
- Discriminative Index: A Novel Indicator for Evaluating Machine Learning Algorithms in Laboratory Medicine.Diagnostics (Basel, Switzerland) · 2026Article
- Vancomycin resistance in gram-positive infections: evolutionary strategies of survival.Archives of microbiology · 2026Review
- ReShuffle-MS: Region-Guided Data Augmentation Improves Artificial Intelligence-Based Resistance Prediction inMicroorganisms · 2026Article
- Rapid prediction of vancomycin-resistantFrontiers in microbiology · 2026Article
- Prediction of antimicrobial resistance from MALDI-TOF mass spectra using machine learning: a validation study.Journal of clinical microbiology · 2025Article
- Novel, rapid, and reliable typing of vancomycin-resistantMicrobiology spectrum · 2025Article
- A pilot study on MALDI-TOF MS-based discrimination of Enterococcus faecalis and Lactococcus spp. isolated from bovine milk samples.World journal of microbiology & biotechnology · 2025Article
- Proteomic Insights into Bacterial Responses to Antibiotics: A Narrative Review.International journal of molecular sciences · 2025Review
- Deep learning-based analysis and identification of single-particle mass spectra of bacteria.Analytical and bioanalytical chemistry · 2025Article
- Spectral Precision: Recent Advances in Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry for Pathogen Detection and Resistance Profiling.Microorganisms · 2025Review
- End-To-End Deep Learning Explains Antimicrobial Resistance in Peak-Picking-Free MALDI-MS Data.Analytical chemistry · 2025Article
- Application of matrix-assisted laser desorption ionization time-of-flight mass spectrometry in the detection of vancomycin-resistant and-susceptibleFrontiers in microbiology · 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
- Repurposing MALDI-TOF MS for effective antibiotic resistance screening in Staphylococcus epidermidis using machine learning.Scientific reports · 2024Article
- Classification ofMicrobiology spectrum · 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
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has recently become a useful analytical approach for microbial identification. The presence and absence of specific peaks on MS spectra are commonly used to identify the bacterial species and predict antibiotic-resistant strains. However, the conventional approach using few single peaks would result in insufficient prediction power without using complete information of whole MS spectra. In the past few years, machine learning algorithms have been successfully applied to analyze the MALDI-TOF MS peaks pattern for rapid strain typing. In this study, we developed a convolutional neural network (CNN) method to deal with the complete information of MALDI-TOF MS spectra for detecting
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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.