ArticlemSystems2024
Predicting
Article in mSystems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review of machine learning on clinical MALDI-TOF MS.Briefings in bioinformatics · 2026Pooled it
- Mapping knowledge landscapes and emerging trends in artificial intelligence for antimicrobial resistance: bibliometric and visualization analysis.Frontiers in medicine · 2025Pooled 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
- Machine Learning-Based Prediction of Antimicrobial Resistance inDiagnostics (Basel, Switzerland) · 2026Article
- Negative-Ion Mode MALDI-TOF MS Combined with Machine Learning for the Rapid Identification of Colistin-ResistantACS omega · 2026Article
- Rapid identification of carbapenem-resistant Acinetobacter baumannii based on MALDI-TOF mass spectrometry and machine learning.BMC microbiology · 2026Article
- Benchmarking deep learning architectures for MALDI-TOF mass spectrometry in infectious disease diagnostics and vector-borne disease surveillance.NPJ digital medicine · 2026Article
- [Advances in integrated antimicrobial resistance diagnostics: quantitative, qualitative and AI-driven approaches].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2026Review
- Clinical application of MALDI-TOF MS for direct and rapid pathogen identification: a comprehensive review focusing on bloodstream infections and sterile body fluids.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2026Review
- AMR-GNN: a multi-representation graph neural network framework to enable genomic antimicrobial resistance prediction.Nature communications · 2026Article
- Combining mass spectrometry and machine learning models for predicting Klebsiella pneumoniae antimicrobial resistance: a multicenter experience from clinical isolates in Italy.BMC microbiology · 2026Article
- Advances in deep learning for antimicrobial research.Frontiers in microbiology · 2026Review
- Antibiotic resistance inFrontiers in microbiology · 2026Review
- Advances in the detection of Drug-Resistant bacteria: current trends and innovations.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2026Review
- Prediction of antimicrobial resistance from MALDI-TOF mass spectra using machine learning: a validation study.Journal of clinical microbiology · 2025Article
- Harnessing machine learning to predict antibiotic susceptibility in Pseudomonas aeruginosa biofilms.NPJ biofilms and microbiomes · 2025Article
- Bacterial Proteomics and Antibiotic Resistance Identification: Is Single-Cell Analysis a Worthwhile Pursuit?Pathogens (Basel, Switzerland) · 2025Review
- Credible inferences in microbiome research: ensuring rigour, reproducibility and relevance in the era of AI.Nature reviews. Gastroenterology & hepatology · 2025Review
- Proteomic Insights into Bacterial Responses to Antibiotics: A Narrative Review.International journal of molecular sciences · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) is widely used in clinical microbiology laboratories for bacterial identification but its use for detection of antimicrobial resistance (AMR) remains limited. Here, we used MALDI-TOF MS with artificial intelligence (AI) approaches to successfully predict AMR in
Indexed as
Identifiers
What OpenQuestion holds
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.