{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T08:01:33Z","timestamp":1780128093241,"version":"3.54.0"},"reference-count":38,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Bioinform Biol Insights"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:p>\n                    Overuse of antimicrobial drugs is known to cause an increase in bacterial resistance among surviving pathogens, reducing the effectiveness of future treatments. Publicly available sequencing collections, such as the National Center for Biotechnology Information Sequence Read Archive (SRA), allow for global investigation of antimicrobial resistance across pathogens. In this study, we developed a pipeline to process 10\u2009803 globally sourced unique bacterial isolates from publicly available SRA datasets (9 pathogens, 4 antibiotics), representing sequencing data generated by multiple laboratories using diverse sequencing platforms and protocols. The pipeline extracted SRA metadata to determine read layout and length, applied quality control, trimming, and decontamination. Preprocessed isolates were mapped reads to an antimicrobial resistance gene class and to a strain-level genome reference library constructed from complete genomes on the SRA submitted between 2009 and 2020. Three classifiers\u2014L1-penalized logistic regression, random forest, and extreme gradient boosting\u2014were trained on the resulting feature matrices, and their outputs were combined in a majority-vote ensemble. Internal training resulted in 83.8% balanced accuracy on average, and external testing yielded 80.2%. Variable importance analyses identified known resistance gene classes and strain markers such as\n                    <jats:italic toggle=\"yes\">Acinetobacter baumannii<\/jats:italic>\n                    LAC-4 and\n                    <jats:italic toggle=\"yes\">Campylobacter jejuni<\/jats:italic>\n                    81-176 and NCTC13255, confirming biological relevance. This work demonstrates a scalable approach for antimicrobial resistance prediction using heterogeneous sequencing data.\n                  <\/jats:p>","DOI":"10.1177\/11779322261453756","type":"journal-article","created":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T05:54:21Z","timestamp":1779861261000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["A Novel Bioinformatics Pipeline and a Machine-Learning Approach for Antimicrobial Resistance Phenotypic Prediction"],"prefix":"10.1177","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4877-0560","authenticated-orcid":false,"given":"Owen","family":"Visser","sequence":"first","affiliation":[{"name":"Department of Biostatistics, University of Florida, Gainesville, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5597-8315","authenticated-orcid":false,"given":"Victor","family":"Agboli","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of Florida, Gainesville, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Somnath","family":"Datta","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of Florida, Gainesville, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,5,26]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/S2666-5247(21)00039-2"},{"key":"e_1_3_3_3_2","article-title":"Unraveling city-specific microbial signatures and identifying sample origins for the data from CAMDA 2020 metagenomic geolocation challenge","volume":"12","author":"Zhang R","year":"2021","unstructured":"Zhang R, Ellis D, Walker AR, Datta S. Unraveling city-specific microbial signatures and identifying sample origins for the data from CAMDA 2020 metagenomic geolocation challenge. Front Genet. 2021;12:659650.","journal-title":"Front Genet"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-020-0894-4"},{"issue":"11","key":"e_1_3_3_5_2","article-title":"Genome-scale rates of evolutionary change in bacteria","volume":"2","author":"Duch\u00eane S","year":"2016","unstructured":"Duch\u00eane S, Holt KE, Weill FX, et al. Genome-scale rates of evolutionary change in bacteria. Microb Genom. 2016;2(11):e000094.","journal-title":"Microb Genom"},{"issue":"2","key":"e_1_3_3_6_2","first-page":"345","article-title":"From development to deployment: dataset shift, causality, and shift-stable models in health AI","volume":"21","author":"Subbaswamy A","year":"2020","unstructured":"Subbaswamy A, Saria S. From development to deployment: dataset shift, causality, and shift-stable models in health AI. Biostatistics. 2020;21(2):345-352.","journal-title":"Biostatistics"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1089\/wound.2014.0560"},{"key":"e_1_3_3_8_2","unstructured":"World Health Organization. WHO publishes list of bacteria for which new antibiotics are urgently needed. 2017. Accessed May 16 2026. https:\/\/www.who.int\/news\/item\/27-02-2017-who-publishes-list-of-bacteria-for-which-new-antibiotics-are-urgently-needed"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.12688\/f1000research.14509.2"},{"key":"e_1_3_3_10_2","unstructured":"National Center for Biotechnology Information. Sequence read archive (SRA). 2025. Accessed May 12 2025. https:\/\/www.ncbi.nlm.nih.gov\/sra"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btu170"},{"key":"e_1_3_3_12_2","unstructured":"Lab TH. Kneaddata: quality control tool for metagenomic data. 2018. 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Resfinder\u2014an open online resource for identification of antimicrobial resistance genes in next-generation sequencing data and prediction of phenotypes from genotypes. Microb Genom. 2022;8(1):000748.","journal-title":"Microb Genom"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkl842"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-018"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btab681"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1128\/cmr.00179-21"},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijantimicag.2022.106684"},{"key":"e_1_3_3_20_2","article-title":"Predicting antimicrobial resistance (AMR) in campylobacter, a foodborne pathogen, and cost burden analysis using machine learning","author":"Mishra S","year":"2025","unstructured":"Mishra S, Han TA, Lopes BS, et al. Predicting antimicrobial resistance (AMR) in campylobacter, a foodborne pathogen, and cost burden analysis using machine learning. arXiv Preprint. 2025; arXiv:250903551.","journal-title":"arXiv Preprint"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijantimicag.2025.107575"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-017-18972-w"},{"key":"e_1_3_3_23_2","doi-asserted-by":"publisher","DOI":"10.3389\/fcimb.2021.610348"},{"key":"e_1_3_3_24_2","unstructured":"UFIT Research Computing. HiPerGator Supercomputing Cluster. University of Florida. 2025. 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