ArticlemSystems2019
Identification of Primary Antimicrobial Resistance Drivers in Agricultural Nontyphoidal Salmonella enterica Serovars by Using Machine Learning.
Article in mSystems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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
15 citing papers in PubMed, 39 citations in OpenAlex.
- Comparison of Machine Learning Classifiers for Predicting Reported Antibiotic Treatment After Recent Fever/Cough Among Under-Five Children: A Pooled Analysis of DHS Data From 64 LMICs.Public health challenges · 2026Article
- Integrated Genome-Wide Association Study and Machine Learning Approach for Characterizing the Determinants of Biofilm Formation in Staphylococcus aureus.Interdisciplinary sciences, computational life sciences · 2026Article
- Early-Warning System for Antimicrobial Resistance inVeterinary sciences · 2025Article
- PredictingMicroorganisms · 2024Article
- Artificial intelligence in predicting pathogenic microorganisms' antimicrobial resistance: challenges, progress, and prospects.Frontiers in cellular and infection microbiology · 2024Review
- Using bacterial pan-genome-based feature selection approach to improve the prediction of minimum inhibitory concentration (MIC).Frontiers in genetics · 2023Article
- Machine learning and feature extraction for rapid antimicrobial resistance prediction ofFrontiers in microbiology · 2023Article
- Article
- Identification of Novel Antimicrobial Resistance Genes Using Machine Learning, Homology Modeling, and Molecular Docking.Microorganisms · 2022Article
- Machine Learning for Antimicrobial Resistance Prediction: Current Practice, Limitations, and Clinical Perspective.Clinical microbiology reviews · 2022Review
- Whole-genome sequencing and gene sharing network analysis powered by machine learning identifies antibiotic resistance sharing between animals, humans and environment in livestock farming.PLoS computational biology · 2022Article
- A review: antimicrobial resistance data mining models and prediction methods study for pathogenic bacteria.The Journal of antibiotics · 2021Review
- A Prediction Method for Animal-Derived Drug Resistance Trend Using a Grey-BP Neural Network Combination Model.Antibiotics (Basel, Switzerland) · 2021Article
- Genomic Characteristics of Colistin-ResistantAntibiotics (Basel, Switzerland) · 2020Article
- UniqueInternational journal of molecular sciences · 2020Article
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 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.