ArticleBMC bioinformatics2022
Enhancing predictions of antimicrobial resistance of pathogens by expanding the potential resistance gene repertoire using a pan-genome-based feature selection approach.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- ALPAR: automated learning pipeline for antimicrobial resistance.Bioinformatics (Oxford, England) · 2026Article
- Next-Generation Target Discovery in ESKAPE Pathogens: An AI-Driven Framework from Omics-Based to Systems-Level Modeling and Clinical Translation.Antibiotics (Basel, Switzerland) · 2026Review
- Artificial intelligence for antimicrobial resistance: advancing reproducibility, interpretability, and clinical deployment.Briefings in bioinformatics · 2026Review
- Antibiotic Resistance Detection and Concomitant Species Identification of ESKAPE Pathogens by Proteomics.Molecular & cellular proteomics : MCP · 2026Article
- Evaluating antimicrobial resistance and clinical outcomes in surgical ICU using a machine learning perspective: a retrospective observational study.BMC infectious diseases · 2025Observational
- Balancing complexity and clarity-towards clinician-ready antibiotic resistance prediction models.Bioinformatics (Oxford, England) · 2025Article
- Prediction of antimicrobial resistance inMicrobiology spectrum · 2025Article
- LC-MS/MS metabolomics unravels the resistant phenotype of carbapenemase-producing Enterobacterales.Metabolomics : Official journal of the Metabolomic Society · 2025Article
- The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance.Computational and structural biotechnology journal · 2025Review
- PanARGMiner (Pan-Genomic Antimicrobial Resistance Gene Miner): An advanced feature selection framework for extracting key resistance genes from pan-genomic datasets.Computational and structural biotechnology journal · 2025Article
- Unitig-centered pan-genome machine learning approach for predicting antibiotic resistance and discovering novel resistance genes in bacterial strains.Computational and structural biotechnology journal · 2024Article
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Artificial intelligence in predicting pathogenic microorganisms' antimicrobial resistance: challenges, progress, and prospects.Frontiers in cellular and infection microbiology · 2024Review
- Artificial intelligence tools for the identification of antibiotic resistance genes.Frontiers in microbiology · 2024Review
- Bioinformatic Analysis Reveals both Oversampled and Underexplored Biosynthetic Diversity in Nonribosomal Peptides.ACS chemical biology · 2023Article
- Using bacterial pan-genome-based feature selection approach to improve the prediction of minimum inhibitory concentration (MIC).Frontiers in genetics · 2023Article
- A Cross-Validated Feature Selection (CVFS) approach for extracting the most parsimonious feature sets and discovering potential antimicrobial resistance (AMR) biomarkers.Computational and structural biotechnology journal · 2023Article
- Carbapenem resistance gene crisis in A. baumannii: a computational analysis.BMC microbiology · 2022Article
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2 authors.
Funding
Abstract
backgroundPredicting which pathogens might exhibit antimicrobial resistance (AMR) based on genomics data is one of the promising ways to swiftly and precisely identify AMR pathogens. Currently, the most widely used genomics approach is through identifying known AMR genes from genomic information in order to predict whether a pathogen might be resistant to certain antibiotic drugs. The list of known AMR genes, however, is still far from comprehensive and may result in inaccurate AMR pathogen predictions. We thus felt the need to expand the AMR gene set and proposed a pan-genome-based feature selection method to identify potential gene sets for AMR prediction purposes.
resultsBy building pan-genome datasets and extracting gene presence/absence patterns from four bacterial species, each with more than 2000 strains, we showed that machine learning models built from pan-genome data can be very promising for predicting AMR pathogens. The gene set selected by the eXtreme Gradient Boosting (XGBoost) feature selection approach further improved prediction outcomes, and an incremental approach selecting subsets of XGBoost-selected features brought the machine learning model performance to the next level. Investigating selected gene sets revealed that on average about 50% of genes had no known function and very few of them were known AMR genes, indicating the potential of the selected gene sets to expand resistance gene repertoires.
conclusionsWe demonstrated that a pan-genome-based feature selection approach is suitable for building machine learning models for predicting AMR pathogens. The extracted gene sets may provide future clues to expand our knowledge of known AMR genes and provide novel hypotheses for inferring bacterial AMR mechanisms.
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