{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T20:10:25Z","timestamp":1791317425239,"version":"4.3.1"},"reference-count":35,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T00:00:00Z","timestamp":1777334400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Microbiol."],"abstract":"\n Background<\/jats:title>\n Antimicrobial resistance (AMR) is an escalating global public health threat that substantially undermines the effectiveness of standard anti-infective therapies and increases the risk of adverse clinical outcomes.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n We analyzed whole-genome sequencing (WGS) data from 1952\n Escherichia coli<\/jats:italic>\n isolates with AST phenotypes. Gene, single-nucleotide polymorphisms (SNPs), and k-mer features were extracted to train machine-learning classifiers for predicting resistance to 10 common antibiotics. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and other classification metrics.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Across all antibiotics and feature representations, model AUCs ranged from 0.6691 to 0.9879. Gene-based and integrated-feature models showed superior and stable performance (AUC 0.8936\u20130.9787 and 0.8888\u20130.9879, respectively) compared with SNP-based models and k-mer models. Prediction performance was near-saturated for aminoglycosides (GEN and TOB) and ciprofloxacin (CIP), whereas \u03b2-lactams exhibited greater heterogeneity, with amoxicillin\/clavulanate (AMX\/CLA) exhibiting the lowest AUC. Feature-importance analysis highlighted 40 core genes that were highly concordant with established resistance mechanisms, including\n aac(3)-IIg<\/jats:italic>\n \/\n aac(3)-IId<\/jats:italic>\n for aminoglycosides,\n blaTEM<\/jats:italic>\n ,\n blaSHV-12<\/jats:italic>\n ,\n blaCTX-M-14<\/jats:italic>\n , and\n blaOXA<\/jats:italic>\n for \u03b2-lactams, and\n tetR(A)<\/jats:italic>\n \/\n tet(A)<\/jats:italic>\n for tetracyclines.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n \n This study demonstrated that machine learning models built on WGS data can accurately and efficiently predict resistance phenotypes to 10 commonly used antibiotics in\n Escherichia coli<\/jats:italic>\n . Among the evaluated feature representations, gene-based and integrated multi-feature approaches yielded the most robust and reliable performance across antibiotic-specific tasks, highlighting the practical utility of WGS-derived genomic features for rapid AMR phenotype prediction and future clinical decision-support applications.\n <\/jats:p>\n <\/jats:sec>","DOI":"10.3389\/fmicb.2026.1842717","type":"journal-article","created":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T05:35:29Z","timestamp":1777354529000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Utilizing whole genome sequencing data for machine learning driven prediction of antibiotic resistance in Escherichia coli"],"prefix":"10.3389","volume":"17","author":[{"given":"Fang","family":"Wan","sequence":"first","affiliation":[{"name":"Department of Laboratory Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing","place":["Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanning","family":"Tong","sequence":"additional","affiliation":[{"name":"Department of Respiratory and Critical Care Medicine, PLA Naval Medical Center","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiwei","family":"Wu","sequence":"additional","affiliation":[{"name":"Dinfectome Inc., Nanjing","place":["Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia","family":"Liu","sequence":"additional","affiliation":[{"name":"Dinfectome Inc., Nanjing","place":["Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Huang","sequence":"additional","affiliation":[{"name":"Dinfectome Inc., Nanjing","place":["Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing","place":["Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenqing","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Wujin Hospital Affiliated with Jiangsu University, Changzhou","place":["Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,4,28]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"e0126020","DOI":"10.1128\/JCM.01260-20","article-title":"Applications of machine learning to the problem of antimicrobial resistance: An emerging model for translational research.","volume":"59","author":"Anahtar","year":"2021","journal-title":"J. Clin. Microbiol."},{"key":"B2","doi-asserted-by":"publisher","first-page":"e18213","DOI":"10.7717\/peerj.18213","article-title":"Integrating whole genome sequencing and machine learning for predicting antimicrobial resistance in critical pathogens: A systematic review of antimicrobial susceptibility tests.","volume":"12","author":"Ardila","year":"2024","journal-title":"PeerJ"},{"key":"B3","doi-asserted-by":"publisher","first-page":"781","DOI":"10.1186\/s12879-020-05499-1","article-title":"Presence of urinary symptoms in bacteremic urinary tract infection: A retrospective cohort study of Escherichia coli bacteremia.","volume":"20","author":"Bai","year":"2020","journal-title":"BMC Infect. Dis."},{"key":"B4","volume-title":"In Silico Predicted Association of Non-Coding RNAs with Antiviral Defence Systems in Bovine-Derived Shiga Toxin-Producing Escherichia coli.","author":"Bialobzyski","year":"2025"},{"key":"B5","doi-asserted-by":"publisher","first-page":"a025247","DOI":"10.1101\/cshperspect.a025247","article-title":"\u03b2-Lactams and \u03b2-lactamase inhibitors: An overview.","volume":"6","author":"Bush","year":"2016","journal-title":"Cold Spring Harb. Perspect. Med."},{"key":"B6","doi-asserted-by":"publisher","first-page":"965","DOI":"10.3390\/antibiotics12060965","article-title":"Antimicrobial resistance: Two-component regulatory systems and multidrug efflux pumps.","volume":"12","author":"De Gaetano","year":"2023","journal-title":"Antibiotics"},{"key":"B7","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.1007\/s15010-023-02163-z","article-title":"Clinical presentation and antimicrobial resistance of invasive Escherichia coli disease in hospitalized older adults: A prospective multinational observational study.","volume":"52","author":"Doua","year":"2024","journal-title":"Infection"},{"key":"B8","doi-asserted-by":"publisher","first-page":"786","DOI":"10.3390\/antibiotics13080786","article-title":"Rapid phenotypic and genotypic antimicrobial susceptibility testing approaches for use in the clinical laboratory.","volume":"13","author":"Hattab","year":"2024","journal-title":"Antibiotics"},{"key":"B9","doi-asserted-by":"publisher","first-page":"105040","DOI":"10.1016\/j.micpath.2021.105040","article-title":"Genetic basis of molecular mechanisms in \u03b2-lactam resistant gram-negative bacteria.","volume":"158","author":"Hussain","year":"2021","journal-title":"Microb. Pathog."},{"key":"B10","doi-asserted-by":"publisher","first-page":"giaa110","DOI":"10.1093\/gigascience\/giaa110","article-title":"Interpreting k-mer\u2013based signatures for antibiotic resistance prediction.","volume":"9","author":"Jaillard","year":"2020","journal-title":"Gigascience"},{"key":"B11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/1040841X.2025.2492156","article-title":"Antibiotic resistance genes in Escherichia coli\u2013literature review.","volume":"52","author":"Kerek","year":"2025","journal-title":"Crit. Rev. Microbiol."},{"key":"B12","doi-asserted-by":"publisher","first-page":"e0017921","DOI":"10.1128\/cmr.00179-21","article-title":"Machine learning for antimicrobial resistance prediction: Current practice, limitations, and clinical perspective.","volume":"35","author":"Kim","year":"2022","journal-title":"Clin. Microbiol. Rev."},{"key":"B13","doi-asserted-by":"publisher","first-page":"e0006525","DOI":"10.1128\/spectrum.00065-25","article-title":"Prediction of antimicrobial resistance in Staphylococcus aureus with a machine learning classifier based on WGS data.","volume":"13","author":"Liu","year":"2025","journal-title":"Microbiol. Spectr."},{"key":"B14","doi-asserted-by":"publisher","first-page":"mgen000491","DOI":"10.1099\/mgen.0.000491","article-title":"Translatability of WGS typing results can simplify data exchange for surveillance and control of Listeria monocytogenes.","volume":"7","author":"Luth","year":"2021","journal-title":"Microb. Genom."},{"key":"B15","doi-asserted-by":"publisher","first-page":"764","DOI":"10.1093\/bioinformatics\/btr011","article-title":"A fast, lock-free approach for efficient parallel counting of occurrences of k-mers.","volume":"27","author":"Mar\u00e7ais","year":"2011","journal-title":"Bioinformatics"},{"key":"B16","doi-asserted-by":"publisher","first-page":"42808","DOI":"10.1038\/srep42808","article-title":"Deciphering the distance to antibiotic resistance for the pneumococcus using genome sequencing data.","volume":"7","author":"Mobegi","year":"2017","journal-title":"Sci. Rep."},{"key":"B17","doi-asserted-by":"publisher","first-page":"1387497","DOI":"10.3389\/fcimb.2024.1387497","article-title":"A review of the mechanisms that confer antibiotic resistance in pathotypes of E. coli.","volume":"14","author":"Nasrollahian","year":"2024","journal-title":"Front. Cell. Infect. Microbiol."},{"key":"B18","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1093\/jac\/dkv312","article-title":"Indirect resistance to several classes of antibiotics in cocultures with resistant bacteria expressing antibiotic-modifying or-degrading enzymes.","volume":"71","author":"Nicoloff","year":"2016","journal-title":"J. Antimicrob. Chemother."},{"key":"B19","doi-asserted-by":"publisher","first-page":"e0000290","DOI":"10.1371\/journal.pdig.0000290","article-title":"Imbalanced class distribution and performance evaluation metrics: A systematic review of prediction accuracy for determining model performance in healthcare systems.","volume":"2","author":"Owusu-Adjei","year":"2023","journal-title":"PLoS Digit. Health"},{"key":"B20","doi-asserted-by":"publisher","first-page":"1610279","DOI":"10.3389\/fmicb.2025.1610279","article-title":"Exploring the eco-evolutionary role of plasmids and defense systems in \u2018Fervidacidithiobacillus caldus\u2019 extreme acidophile.","volume":"16","author":"Pacheco-Acosta","year":"2025","journal-title":"Front. Microbiol."},{"key":"B21","doi-asserted-by":"publisher","first-page":"953","DOI":"10.1186\/s12879-022-07837-x","article-title":"Clinical outcomes and prognostic factors of patients with sepsis caused by intra-abdominal infection in the intensive care unit: A post-hoc analysis of a prospective cohort study in Korea.","volume":"22","author":"Park","year":"2022","journal-title":"BMC Infect. Dis."},{"key":"B22","doi-asserted-by":"publisher","first-page":"1887","DOI":"10.3389\/fmicb.2016.01887","article-title":"Evaluation of machine learning and rules-based approaches for predicting antimicrobial resistance profiles in gram-negative bacilli from whole genome sequence data.","volume":"7","author":"Pesesky","year":"2016","journal-title":"Front. Microbiol."},{"key":"B23","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1111\/2041-210x.13915","article-title":"Improving the predictability and interpretability of co-occurrence modelling through feature-based joint species distribution ensembles.","volume":"14","author":"Powell-Romero","year":"2023","journal-title":"Methods Ecol. Evol."},{"key":"B24","doi-asserted-by":"publisher","first-page":"e0048825","DOI":"10.1128\/mbio.00488-25","article-title":"Evolutionary accumulation modeling in AMR: Machine learning to infer and predict evolutionary dynamics of multi-drug resistance.","volume":"16","author":"Renz","year":"2025","journal-title":"mBio"},{"key":"B25","doi-asserted-by":"publisher","first-page":"452","DOI":"10.3390\/antibiotics12030452","article-title":"Using machine learning to predict antimicrobial resistance\u2014a literature review.","volume":"12","author":"Sakagianni","year":"2023","journal-title":"Antibiotics"},{"key":"B26","doi-asserted-by":"publisher","first-page":"000685","DOI":"10.1099\/mgen.0.000685","article-title":"Bakta: Rapid and standardized annotation of bacterial genomes via alignment-free sequence identification.","volume":"7","author":"Schwengers","year":"2021","journal-title":"Microb. Genom."},{"key":"B27","doi-asserted-by":"publisher","first-page":"msad028","DOI":"10.1093\/molbev\/msad028","article-title":"Horizontal gene transfer, fitness costs and mobility shape the spread of antibiotic resistance genes into experimental populations of Acinetobacter baylyi.","volume":"40","author":"Sezmis","year":"2023","journal-title":"Mol. Biol. Evol."},{"key":"B28","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1038\/s41540-025-00584-0","article-title":"Minimal gene signatures enable high-accuracy prediction of antibiotic resistance in Pseudomonas aeruginosa.","volume":"11","author":"Shahreen","year":"2025","journal-title":"NPJ Syst. Biol. Appl."},{"key":"B29","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1186\/s12859-019-3054-4","article-title":"Antimicrobial resistance genetic factor identification from whole-genome sequence data using deep feature selection.","volume":"20","author":"Shi","year":"2019","journal-title":"BMC Bioinformatics"},{"key":"B30","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1038\/s41579-020-0327-x","article-title":"Innovative and rapid antimicrobial susceptibility testing systems.","volume":"18","author":"van Belkum","year":"2020","journal-title":"Nat. Rev. Microbiol."},{"key":"B31","doi-asserted-by":"publisher","first-page":"1013","DOI":"10.3389\/fmicb.2020.01013","article-title":"Prediction of antimicrobial resistance in gram-negative bacteria from whole-genome sequencing data.","volume":"11","author":"Van Camp","year":"2020","journal-title":"Front. Microbiol."},{"key":"B32","doi-asserted-by":"publisher","first-page":"1664","DOI":"10.1016\/j.molp.2022.09.001","article-title":"Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction.","volume":"15","author":"Xu","year":"2022","journal-title":"Mol. Plant"},{"key":"B33","doi-asserted-by":"publisher","first-page":"e00115-20","DOI":"10.1128\/CMR.00115-20","article-title":"New \u03b2-lactam\u2013\u03b2-lactamase inhibitor combinations.","volume":"34","author":"Yahav","year":"2020","journal-title":"Clin. Microbiol. Rev."},{"key":"B34","doi-asserted-by":"publisher","first-page":"e0251224","DOI":"10.1128\/aem.02512-24","article-title":"Bacterial genome-wide association studies: Exploring the genetic variation underlying bacterial phenotypes.","volume":"91","author":"Yang","year":"2025","journal-title":"Appl. Environ. Microbiol."},{"key":"B35","doi-asserted-by":"publisher","first-page":"100972","DOI":"10.1016\/j.lanwpc.2023.100972","article-title":"Burden of infectious diseases and bacterial antimicrobial resistance in China: A systematic analysis for the global burden of disease study 2019.","volume":"43","author":"Zhang","year":"2024","journal-title":"Lancet Reg. Health Western Pac."}],"container-title":["Frontiers in Microbiology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmicb.2026.1842717\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T05:35:30Z","timestamp":1777354530000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmicb.2026.1842717\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,28]]},"references-count":35,"alternative-id":["10.3389\/fmicb.2026.1842717"],"URL":"https:\/\/doi.org\/10.3389\/fmicb.2026.1842717","relation":{},"ISSN":["1664-302X"],"issn-type":[{"value":"1664-302X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,28]]},"article-number":"1842717"}}