SynthesisPLoS computational biology2019
Evaluation of parameters affecting performance and reliability of machine learning-based antibiotic susceptibility testing from whole genome sequencing data.
Synthesis in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 2 of them syntheses 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
48 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning.Sensors (Basel, Switzerland) · 2021Pooled it
- Large-scale assessment of antimicrobial resistance marker databases for genetic phenotype prediction: a systematic review.The Journal of antimicrobial chemotherapy · 2020Pooled it
- Staphylococcus aureus resistance to non-β-lactam antibiotics: global genomic epidemiology and machine learning feasibility assessment.Molecular genetics and genomics : MGG · 2026Article
- A multi-scale transformer-enhanced YOLO framework for unified road damage detection and boundary-aware segmentation.Frontiers in artificial intelligence · 2026Article
- StaLAENet: A stacked LSTM-nested deep-autoencoder network for identification of antimicrobial resistance of nosocomial pathogens.Journal of biosciences · 2026Article
- Biased sampling driven by bacterial population structure confounds machine learning prediction of antimicrobial resistance.PLoS biology · 2025Article
- Genomics for antimicrobial resistance-progress and future directions.Antimicrobial agents and chemotherapy · 2025Review
- Article
- Advancements in AI-driven drug sensitivity testing research.Frontiers in cellular and infection microbiology · 2025Review
- Antimicrobial resistance at a turning point: microbial drivers, one health, and global futures.Frontiers in microbiology · 2025Review
- Keeping up with the pathogens: improved antimicrobial resistance detection and prediction from Pseudomonas aeruginosa genomes.Genome medicine · 2024Article
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Optimising machine learning prediction of minimum inhibitory concentrations inMicrobial genomics · 2024Article
- National genomic surveillance integrating standardized quantitative susceptibility testing clarifies antimicrobial resistance in Enterobacterales.Nature communications · 2023Article
- Predictive modeling of antibiotic eradication therapy success for new-onset Pseudomonas aeruginosa pulmonary infections in children with cystic fibrosis.PLoS computational biology · 2023Article
- From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry.FEMS microbiology reviews · 2023Review
- Article
- Contemporary Considerations for Establishing Reference Methods for Antibacterial Susceptibility Testing.Journal of clinical microbiology · 2023Review
- Antibiotic Resistance Diagnosis in ESKAPE Pathogens-A Review on Proteomic Perspective.Diagnostics (Basel, Switzerland) · 2023Review
- Artificial Intelligence for Antimicrobial Resistance Prediction: Challenges and Opportunities towards Practical Implementation.Antibiotics (Basel, Switzerland) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Prediction of antibiotic resistance phenotypes from whole genome sequencing data by machine learning methods has been proposed as a promising platform for the development of sequence-based diagnostics. However, there has been no systematic evaluation of factors that may influence performance of such models, how they might apply to and vary across clinical populations, and what the implications might be in the clinical setting. Here, we performed a meta-analysis of seven large Neisseria gonorrhoeae datasets, as well as Klebsiella pneumoniae and Acinetobacter baumannii datasets, with whole genome sequence data and antibiotic susceptibility phenotypes using set covering machine classification, random forest classification, and random forest regression models to predict resistance phenotypes from genotype. We demonstrate how model performance varies by drug, dataset, resistance metric, and species, reflecting the complexities of generating clinically relevant conclusions from machine learning-derived models. Our findings underscore the importance of incorporating relevant biological and epidemiological knowledge into model design and assessment and suggest that doing so can inform tailored modeling for individual drugs, pathogens, and clinical populations. We further suggest that continued comprehensive sampling and incorporation of up-to-date whole genome sequence data, resistance phenotypes, and treatment outcome data into model training will be crucial to the clinical utility and sustainability of machine learning-based molecular diagnostics.
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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.