ArticlemSystems2020
Prediction of Acquired Antimicrobial Resistance for Multiple Bacterial Species Using Neural Networks.
Article in mSystems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis 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
40 citing papers in PubMed, 1 synthesis or guideline pooled it, 66 citations in OpenAlex.
- Pooled it
- Article
- Prediction of antimicrobial minimum inhibitory concentration from bacterial genomes using a scalable and interpretable machine learning approach.npj antimicrobials and resistance · 2026Article
- Artificial intelligence for antimicrobial resistance: advancing reproducibility, interpretability, and clinical deployment.Briefings in bioinformatics · 2026Review
- Artificial Neural Network Elucidates the Role of Transport Proteins inMetabolites · 2026Article
- Harnessing the gut microbiome to combat tuberculosis: a technological and clinical review.Frontiers in cellular and infection microbiology · 2026Review
- Harnessing interpretable deep learning to predict resistance inFrontiers in cellular and infection microbiology · 2026Article
- stackPredAMR-a stacked random forest approach improves AMR phenotype prediction for multiple species and antimicrobial agents.Bioinformatics advances · 2026Article
- A comparative analysis of graph neural networks and classical machine learning for carbapenem-resistance prediction and genomic interpretation inFrontiers in bioinformatics · 2026Article
- Unlocking antimicrobial resistance with multiomics and machine learning.Trends in microbiology · 2025Article
- Assessing the Antibiotic Resistance in Food Lactic Acid Bacteria: Risks in the Era of Widespread Probiotic Use.Food science & nutrition · 2025Review
- Proteomic Insights into Bacterial Responses to Antibiotics: A Narrative Review.International journal of molecular sciences · 2025Review
- The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance.Computational and structural biotechnology journal · 2025Review
- Neural network-based predictions of antimicrobial resistance phenotypes in multidrug-resistantAntimicrobial agents and chemotherapy · 2024Article
- Review
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomes.Briefings in bioinformatics · 2024Article
- Leveraging Large Data, Statistics, and Machine Learning to Predict the Emergence of ResistantPharmacy (Basel, Switzerland) · 2024Article
- PredictingMicroorganisms · 2024Article
- Artificial intelligence in predicting pathogenic microorganisms' antimicrobial resistance: challenges, progress, and prospects.Frontiers in cellular and infection microbiology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning has proven to be a powerful method to predict antimicrobial resistance (AMR) without using prior knowledge for selected bacterial species-antimicrobial combinations. To date, only species-specific machine learning models have been developed, and to the best of our knowledge, the inclusion of information from multiple species has not been attempted. The aim of this study was to determine the feasibility of including information from multiple bacterial species to predict AMR for an individual species, since this may make it easier to train and update resistance predictions for multiple species and may lead to improved predictions. Whole-genome sequence data and susceptibility profiles from 3,528
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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.