{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T16:55:21Z","timestamp":1791392121226,"version":"4.3.3"},"reference-count":77,"publisher":"Microbiology Society","issue":"10","license":[{"start":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T00:00:00Z","timestamp":1791331200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science and Technology Council","award":["NSTC114-2221-E-038-026-MY3"],"award-info":[{"award-number":["NSTC114-2221-E-038-026-MY3"]}]},{"name":"National Science and Technology Council","award":["NSTC114-2221-E-038-027-MY3"],"award-info":[{"award-number":["NSTC114-2221-E-038-027-MY3"]}]},{"name":"National Science and Technology Council","award":["NSTC115-2119-M-001-011"],"award-info":[{"award-number":["NSTC115-2119-M-001-011"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,10,7]]},"abstract":"<jats:p>\n                    <jats:bold>Background.<\/jats:bold>\n                    Antimicrobial resistance is one of the biggest threats to modern medicine, yet our understanding of its genetic basis remains incomplete. Current surveillance strategies rely on established resistance genes catalogued in curated databases; however, bacterial populations continuously evolve resistance mechanisms that may not be recognized or characterized in those existing catalogues. Here, we combined pan-genomic analysis with machine-learning feature selection to identify genes significantly associated with antibiotic resistance in\n                    <jats:italic>Klebsiella pneumoniae<\/jats:italic>\n                    and to explore potential resistance determinants that have not been recognized before.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Results.<\/jats:bold>\n                    A total of 8,132\n                    <jats:italic>K<\/jats:italic>\n                    .\n                    <jats:italic>pneumoniae<\/jats:italic>\n                    genomes were collected to construct a comprehensive pan-genome comprising &gt;180,000\u2009gene families. Using AdaBoost with an incremental feature-selection approach, we identified parsimonious gene sets (14\u201327 genes per antibiotic) that achieved high predictive accuracy (mean area under the receiver operating characteristic curve: 0.96; F\n                    <jats:sub>1<\/jats:sub>\n                    -score: 0.93). Notably, about 15.8% of these genes were previously annotated as antimicrobial resistance determinants, whereas hypothetical proteins (HPs) and mobile genetic elements accounted for 32.5 and 22.3%, respectively. Among known resistance genes, we found that the\n                    <jats:italic>ble<\/jats:italic>\n                    gene, which encodes the bleomycin resistance protein, was widely associated with resistance to 8 of 12 included antibiotics. The functional re-annotation process also identified 15 HPs with diverse functions that are highly relevant to resistance, including transposases, components of the plasmid mobilization machinery, DNA manipulation proteins and regulatory factors. These findings underscore the importance of selecting and analysing highly associated features (or genes) in bacterial resistance.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Conclusion.<\/jats:bold>\n                    The study shows that pan-genomic analysis, coupled with feature selection, can uncover resistance-associated genes that conventional approaches miss. The findings also support enhancing surveillance strategies that incorporate emerging resistance mechanisms through large-scale genomic analysis, rather than relying exclusively on catalogued repertoires.\n                  <\/jats:p>","DOI":"10.1099\/mgen.0.001851","type":"journal-article","created":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T16:17:18Z","timestamp":1791389838000},"source":"Crossref","is-referenced-by-count":0,"title":["Uncovering multidrug-resistance genes and potential biomarkers in Klebsiella pneumoniae using pan-genomic machine-learning feature-selection approach"],"prefix":"10.1099","volume":"12","author":[{"given":"Thi Phuong","family":"Nguyen","sequence":"first","affiliation":[{"name":"Health Innovation Center, National Yang-Ming Chiao-Tung University, 155 Section 2, Linong St., Beitou District, Taipei, Taiwan, ROC"},{"name":"Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, 250 Wuxing St., Xinyi District, Taipei 11031, Taiwan, ROC"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hsuan-Chia","family":"Yang","sequence":"additional","affiliation":[{"name":"International Center for Health Information and Technology, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan, ROC"},{"name":"Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, 250 Wuxing St., Xinyi District, Taipei 11031, Taiwan, ROC"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5603-1194","authenticated-orcid":false,"given":"Yu-Wei","family":"Wu","sequence":"additional","affiliation":[{"name":"TMU Research Center for Digestive Medicine, Taipei Medical University, Taipei, Taiwan, ROC"},{"name":"Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, 250 Wuxing St., Xinyi District, Taipei 11031, Taiwan, ROC"},{"name":"Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei, Taiwan, ROC"},{"name":"Interdisciplinary Undergraduate Program in Intelligent Healthcare, College of Interdisciplinary Studies, Taipei Medical University, Taipei, Taiwan, ROC"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"345","reference":[{"key":"R1","doi-asserted-by":"publisher","DOI":"10.1080\/21505594.2016.1159366"},{"key":"R2","doi-asserted-by":"publisher","DOI":"10.1016\/j.lanmic.2024.07.010"},{"key":"R3","article-title":"Global antimicrobial resistance and use surveillance system (GLASS) report 2022","year":"2022"},{"key":"R4","volume-title":"Final Report (English). 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