ArticleJournal of clinical microbiology2019
Using Machine Learning To Predict Antimicrobial MICs and Associated Genomic Features for Nontyphoidal
Article in Journal of clinical microbiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 148 papers, 3 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
148 citing papers in PubMed, 3 syntheses or guidelines pooled it, 278 citations in OpenAlex.
- Antimicrobial resistance crisis: could artificial intelligence be the solution?Military Medical Research · 2024Pooled it
- Systematic review of surveillance systems for AMR in Africa.The Journal of antimicrobial chemotherapy · 2022Pooled it
- Evaluation of parameters affecting performance and reliability of machine learning-based antibiotic susceptibility testing from whole genome sequencing data.PLoS computational biology · 2019Pooled it
- Artificial Intelligence and Bioengineering Approaches for Antimicrobial Resistance Prediction.Medicina (Kaunas, Lithuania) · 2026Review
- ALPAR: automated learning pipeline for antimicrobial resistance.Bioinformatics (Oxford, England) · 2026Article
- Uncovering species- and drug-class-specific antimicrobial resistance mechanisms from large-scale whole-genome sequencing data using discordance analysis and machine learning.Briefings in bioinformatics · 2026Article
- Machine learning-driven identification of virulence determinants in Borrelia burgdorferi associated with human dissemination.PLoS computational biology · 2026Article
- 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
- Integrating Phenotypic and Genomic Data with Machine Learning to Predict Antimicrobial Resistance and Identify Genetic Biomarkers inInternational journal of environmental research and public health · 2026Article
- Evaluation of a machine learning system for genomic antimicrobial susceptibility determination on a clinically representative test set.Microbiology spectrum · 2026Article
- Article
- From surveillance to intelligence: a scoping review of machine learning for antimicrobial resistance surveillance intelligence across One Health.Frontiers in public health · 2026Article
- Molecular mechanisms of antibiotic resistance in Gram-positive and Gram-negative bacteria - a narrative review.Frontiers in cellular and infection microbiology · 2026Review
- Advances in deep learning for antimicrobial research.Frontiers in microbiology · 2026Review
- Antibiotic Resistance Crisis: From Bacterial Bioprospecting to Artificial Intelligence.Environmental microbiology reports · 2025Review
- Early-Warning System for Antimicrobial Resistance inVeterinary sciences · 2025Article
- Emerging technologies for rapid phenotypic antimicrobial susceptibility testing of clinical isolates of bacteria.Journal of clinical microbiology · 2025Review
- Harnessing machine learning to predict antibiotic susceptibility in Pseudomonas aeruginosa biofilms.NPJ biofilms and microbiomes · 2025Article
- Unveiling meropenem resistance and co-resistance patterns in Klebsiella pneumoniae and Acinetobacter baumannii: a global genome analysis using ML/DL and association mining.BMC microbiology · 2025Article
88 more citing papers are in PubMed but not listed here.
Corrections and comments
- Commented on by
Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
Nontyphoidal
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.