SynthesisPeerJ2024
Integrating whole genome sequencing and machine learning for predicting antimicrobial resistance in critical pathogens: a systematic review of antimicrobial susceptibility tests.
Synthesis in PeerJ, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- Artificial Intelligence and Bioengineering Approaches for Antimicrobial Resistance Prediction.Medicina (Kaunas, Lithuania) · 2026Review
- Innovative approaches to therapeutic target discovery amid the global challenge of antimicrobial resistance.Journal of computer-aided molecular design · 2026Review
- Review of risk factors and prediction models for multidrug-resistant bacterial pulmonary infections in patients with chronic obstructive pulmonary disease.The Journal of international medical research · 2026Review
- Improving the Precision of Etiological Diagnosis in Bacterial Infections Using Molecular Technologies: A Comparative Analysis of Platforms, AI Integration, and Point-of-Care Deployment.International journal of molecular sciences · 2026Review
- Artificial intelligence for antimicrobial resistance: advancing reproducibility, interpretability, and clinical deployment.Briefings in bioinformatics · 2026Review
- Phenotype-Genotype Discordance in Antimicrobial Resistance ofPathogens (Basel, Switzerland) · 2026Review
- DetectingAntibiotics (Basel, Switzerland) · 2026Article
- Vancomycin resistance in gram-positive infections: evolutionary strategies of survival.Archives of microbiology · 2026Review
- Artificial intelligence for improving decision-making in bacterial infection management: a narrative review.The Journal of antimicrobial chemotherapy · 2026Review
- Utilizing whole genome sequencing data for machine learning driven prediction of antibiotic resistance inFrontiers in microbiology · 2026Article
- Integrating artificial intelligence with genome sequencing against antimicrobial resistance: a narrative review.Frontiers in public health · 2026Review
- Harnessing interpretable deep learning to predict resistance inFrontiers in cellular and infection microbiology · 2026Article
- 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
- Minimum Inhibitory Concentrations of Anidulafungin as a Potential Predictor of Biocide Susceptibility of Clade 1 Candidozyma (Candida) auris Isolates.MicrobiologyOpen · 2025Article
- Nickel Bioaccumulation in Oral Biofilm, Gingival Tissue, and Saliva During Fixed Orthodontic Treatment: A 12-Month Prospective Cohort Study on Predictors and Salivary pH Correlation.Journal of clinical and experimental dentistry · 2025Article
- Characterization of Penicillin-Non-Susceptible, Multidrug-Resistant Streptococcus agalactiae Using Whole-Genome Sequencing.Current microbiology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Infections caused by antibiotic-resistant bacteria pose a major challenge to modern healthcare. This systematic review evaluates the efficacy of machine learning (ML) approaches in predicting antimicrobial resistance (AMR) in critical pathogens (CP), considering Whole Genome Sequencing (WGS) and antimicrobial susceptibility testing (AST). Methods: The search covered databases including PubMed/MEDLINE, EMBASE, Web of Science, SCOPUS, and SCIELO, from their inception until June 2024. The review protocol was officially registered on PROSPERO (CRD42024543099). Results: The review included 26 papers, analyzing data from 104,141 microbial samples. Random Forest (RF), XGBoost, and logistic regression (LR) emerged as the top-performing models, with mean Area Under the Receiver Operating Characteristic (AUC) values of 0.89, 0.87, and 0.87, respectively. RF showed superior performance with AUC values ranging from 0.66 to 0.97, while XGBoost and LR showed similar performance with AUC values ranging from 0.83 to 0.91 and 0.76 to 0.96, respectively. Most studies indicate that integrating WGS and AST data into ML models enhances predictive performance, improves antibiotic stewardship, and provides valuable clinical decision support. ML shows significant promise for predicting AMR by integrating WGS and AST data in CP. Standardized guidelines are needed to ensure consistency in future research.
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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.