ArticleFrontiers in microbiology2020
Prediction of Antimicrobial Resistance in Gram-Negative Bacteria From Whole-Genome Sequencing Data.
Article in Frontiers in microbiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed, 71 citations in OpenAlex.
- Optimisation and validation of capture mNGS for predicting antimicrobial resistance.EBioMedicine · 2026Article
- StaLAENet: A stacked LSTM-nested deep-autoencoder network for identification of antimicrobial resistance of nosocomial pathogens.Journal of biosciences · 2026Article
- stackPredAMR-a stacked random forest approach improves AMR phenotype prediction for multiple species and antimicrobial agents.Bioinformatics advances · 2026Article
- From surveillance to intelligence: a scoping review of machine learning for antimicrobial resistance surveillance intelligence across One Health.Frontiers in public health · 2026Article
- Advances in deep learning for antimicrobial research.Frontiers in microbiology · 2026Review
- Utilizing whole genome sequencing data for machine learning driven prediction of antibiotic resistance inFrontiers in microbiology · 2026Article
- Prediction of antimicrobial resistance inMicrobiology spectrum · 2025Article
- Prediction of antibiotic resistance from antibiotic susceptibility testing results from surveillance data using machine learning.Scientific reports · 2025Article
- Novel Antibacterial Approaches and Therapeutic Strategies.Antibiotics (Basel, Switzerland) · 2025Review
- Integrating Machine Learning with MALDI-TOF Mass Spectrometry for Rapid and Accurate Antimicrobial Resistance Detection in Clinical Pathogens.International journal of molecular sciences · 2025Article
- Therapeutic approaches for septicemia induced by multidrug-resistant bacteria using desert-adapted plants.Frontiers in cellular and infection microbiology · 2025Article
- Advancements in AI-driven drug sensitivity testing research.Frontiers in cellular and infection microbiology · 2025Review
- Computational analyses of drug resistance mutations in katG and emb complexes in Mycobacterium tuberculosis.Proteins · 2025Article
- Neural network-based predictions of antimicrobial resistance phenotypes in multidrug-resistantAntimicrobial agents and chemotherapy · 2024Article
- Presence of Methicillin-Resistant Staphylococci and Carbapenemase-PositiveVeterinary sciences · 2024Article
- A perspective-driven and technical evaluation of machine learning in bioreactor scale-up: A case-study for potential model developments.Engineering in life sciences · 2024Article
- 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
- MGS2AMR: a gene-centric mining of metagenomic sequencing data for pathogens and their antimicrobial resistance profile.Microbiome · 2023Article
- Pipeline validation for the identification of antimicrobial-resistant genes in carbapenem-resistant Klebsiella pneumoniae.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEarly detection of antimicrobial resistance in pathogens and prescription of more effective antibiotics is a fast-emerging need in clinical practice. High-throughput sequencing technology, such as whole genome sequencing (WGS), may have the capacity to rapidly guide the clinical decision-making process. The prediction of antimicrobial resistance in Gram-negative bacteria, often the cause of serious systemic infections, is more challenging as genotype-to-phenotype (drug resistance) relationship is more complex than for most Gram-positive organisms. METHODS AND
findingsWe have used NCBI BioSample database to train and cross-validate eight XGBoost-based machine learning models to predict drug resistance to cefepime, cefotaxime, ceftriaxone, ciprofloxacin, gentamicin, levofloxacin, meropenem, and tobramycin tested in
conclusionWhole genome sequencing enables the prediction of antimicrobial resistance in Gram-negative bacteria. We present a tool that provides an
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