{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T00:18:15Z","timestamp":1760660295223,"version":"build-2065373602"},"reference-count":10,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>The escalating challenge of antibiotic resistance (ABR) demands clinician-ready machine learning models that are not only accurate but interpretable.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>By treating resistance genes as independent features and augmenting them with curated single-nucleotide polymorphisms and contextual markers, this approach delivers scalable, transparent predictions aligned with clinical decision-making needs.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Not applicable.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf556","type":"journal-article","created":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T15:15:38Z","timestamp":1759331738000},"source":"Crossref","is-referenced-by-count":0,"title":["Balancing complexity and clarity\u2014towards clinician-ready antibiotic resistance prediction models"],"prefix":"10.1093","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2818-7682","authenticated-orcid":false,"given":"Dickson","family":"Aruhomukama","sequence":"first","affiliation":[{"name":"Department of Medical Microbiology, College of Health Sciences, Makerere University (MakCHS) , Kampala, 7072,","place":["Uganda"]},{"name":"Department of Global Health Security, The Infectious Diseases Institute (IDI), MakCHS , Kampala, 22418,","place":["Uganda"]}]}],"member":"286","published-online":{"date-parts":[[2025,10,1]]},"reference":[{"year":"2024","author":"Bai","key":"2025101607393730900_btaf556-B1"},{"key":"2025101607393730900_btaf556-B2","doi-asserted-by":"crossref","first-page":"e00179-21","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 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