{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T15:51:24Z","timestamp":1786117884848,"version":"build-2736575974"},"reference-count":32,"publisher":"American Society for Microbiology","issue":"8","license":[{"start":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T00:00:00Z","timestamp":1785801600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T00:00:00Z","timestamp":1785801600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.asm.org\/non-commercial-tdm-license"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82372298, 32170160"],"award-info":[{"award-number":["82372298, 32170160"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.asm.org"],"crossmark-restriction":true},"short-container-title":["Microbiol Spectr"],"published-print":{"date-parts":[[2026,8,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title\/>\n                    <jats:p>\n                      The emergence of bacterial resistance to antibiotics poses a significant threat to human health; thus, there is an urgent need for new strategies in understanding the mechanisms of resistance and further fast prediction of it. Deep learning models offer promising solutions through analyzing genetic sequences in the prediction of bacterial resistance patterns. This study develops and validates a transformer-based deep learning model, DNABERT-2-117M, to predict gentamicin resistance in\n                      <jats:italic toggle=\"yes\">Klebsiella pneumoniae<\/jats:italic>\n                      directly from whole-genome sequences. Our central methodological advance investigates the impact of the DNA tokenization strategy on predictive performance. We prospectively compared a dynamic tokenization approach against conventional fixed-length tokenization. Evaluated through rigorous fivefold cross-validation and on a hold-out test set, the model employing dynamic tokenization achieved superior performance, with a mean F1-score of 0.95 and an area under the curve of 0.97. Our findings establish that optimizing sub-sequence tokenization is crucial for model accuracy, and this dynamic tokenization approach significantly enhances model accuracy for antibiotic resistance prediction from genomic data. This genome-based predictive model represents a scalable and rapid alternative to traditional antibiotic susceptibility testing, offering the potential to accelerate clinical decision-making and improve patient outcomes in managing\n                      <jats:italic toggle=\"yes\">K. pneumoniae<\/jats:italic>\n                      infections.\n                    <\/jats:p>\n                    <jats:sec>\n                      <jats:title>IMPORTANCE<\/jats:title>\n                      <jats:p>\n                        This study addresses a critical gap in diagnostic technologies for hypervirulent, antibiotic-resistant\n                        <jats:italic toggle=\"yes\">Klebsiella pneumoniae<\/jats:italic>\n                        . We introduce a transformer-based deep learning framework that utilizes dynamic tokenization strategies to predict drug resistance directly from genomic sequences. The core significance of our work is the development of a robust genomic prediction model that serves as a foundational component for future diagnostic paradigms. The significance of this work lies in its potential to fundamentally alter clinical timelines. By decoupling resistance prediction from the requirement for phenotypic growth, our approach is a critical step toward next-generation workflows (e.g., clinical metagenomics) that could deliver a complete diagnostic and susceptibility report directly from a patient sample within hours. This represents a scalable, rapid diagnostic platform that promises to accelerate the administration of targeted treatment for high-risk\n                        <jats:italic toggle=\"yes\">K. pneumoniae<\/jats:italic>\n                        infections, with a clear trajectory toward same-day, specimen-to-result diagnostics in the near future.\n                      <\/jats:p>\n                    <\/jats:sec>\n                  <\/jats:sec>","DOI":"10.1128\/spectrum.01906-25","type":"journal-article","created":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T13:02:36Z","timestamp":1784120556000},"update-policy":"https:\/\/doi.org\/10.1128\/asmj-crossmark-policy-page","source":"Crossref","is-referenced-by-count":0,"title":["Construction and validation of a phenotypic prediction model for bacterial gentamicin resistance using deep learning with gene sequences"],"prefix":"10.1128","volume":"14","author":[{"given":"Jun","family":"Li","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/045kpgw45","id-type":"ROR","asserted-by":"publisher"}],"name":"Laboratory Medicine, Guangdong Provincial People\u2019s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siyan","family":"Xue","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/03cve4549","id-type":"ROR","asserted-by":"publisher"}],"name":"School of Biomedical Engineering, Tsinghua University","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingxuan","family":"Hou","sequence":"additional","affiliation":[{"name":"Optical Bioimaging Laboratory, Department of Biomedical Engineering, National University of Singapore","place":["Singapore, Singapore"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhixuan","family":"Zhang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/045kpgw45","id-type":"ROR","asserted-by":"publisher"}],"name":"Laboratory Medicine, Guangdong Provincial People\u2019s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University","place":["Guangzhou, China"]},{"name":"School of Medicine, South China University of Technology","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9815-7846","authenticated-orcid":true,"given":"Kaixuan","family":"Yuan","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/045kpgw45","id-type":"ROR","asserted-by":"publisher"}],"name":"Laboratory Medicine, Guangdong Provincial People\u2019s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaozhong","family":"Chen","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/045kpgw45","id-type":"ROR","asserted-by":"publisher"}],"name":"Laboratory Medicine, Guangdong Provincial People\u2019s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3855-5867","authenticated-orcid":false,"given":"Chui","family":"Kong","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Fudan University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liyang","family":"Wang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/03cve4549","id-type":"ROR","asserted-by":"publisher"}],"name":"School of Biomedical Engineering, Tsinghua University","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7764-3213","authenticated-orcid":true,"given":"Bing","family":"Gu","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/045kpgw45","id-type":"ROR","asserted-by":"publisher"}],"name":"Laboratory Medicine, Guangdong Provincial People\u2019s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University","place":["Guangzhou, China"]},{"name":"School of Medicine, South China University of Technology","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9401-9770","authenticated-orcid":true,"given":"Xiaoxiao","family":"Liu","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/045kpgw45","id-type":"ROR","asserted-by":"publisher"}],"name":"Laboratory Medicine, Guangdong Provincial People\u2019s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University","place":["Guangzhou, China"]},{"name":"School of Medicine, South China University of Technology","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"235","reference":[{"key":"e_1_3_4_2_2","volume-title":"National strategy for combating antibiotic-resistant bacteria","author":"The White House","year":"2015","unstructured":"The White House. 2015. National strategy for combating antibiotic-resistant bacteria. The White House, Washington, DC."},{"key":"e_1_3_4_3_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41579-021-00649-x"},{"key":"e_1_3_4_4_2","doi-asserted-by":"publisher","DOI":"10.3390\/w16172362"},{"key":"e_1_3_4_5_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41579-021-00534-7"},{"key":"e_1_3_4_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(21)02724-0"},{"key":"e_1_3_4_7_2","doi-asserted-by":"publisher","DOI":"10.1128\/CMR.11.4.589"},{"key":"e_1_3_4_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/S1473-3099(17)30489-9"},{"key":"e_1_3_4_9_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btab083"},{"key":"e_1_3_4_10_2","doi-asserted-by":"publisher","DOI":"10.1001\/archinte.1991.00400080059010"},{"key":"e_1_3_4_11_2","doi-asserted-by":"publisher","DOI":"10.1080\/14787210.2019.1555470"},{"key":"e_1_3_4_12_2","doi-asserted-by":"publisher","DOI":"10.1093\/cid\/cix270"},{"key":"e_1_3_4_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.it.2023.08.005"},{"key":"e_1_3_4_14_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41564-021-00870-7"},{"key":"e_1_3_4_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.micres.2024.127701"},{"key":"e_1_3_4_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2024.05.013"},{"key":"e_1_3_4_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bios.2024.116301"},{"key":"e_1_3_4_18_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btae243"},{"key":"e_1_3_4_19_2","doi-asserted-by":"publisher","DOI":"10.1080\/15598608.2017.1353456"},{"key":"e_1_3_4_20_2","doi-asserted-by":"publisher","DOI":"10.1101\/gr.200535.115"},{"key":"e_1_3_4_21_2","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.3547"},{"key":"e_1_3_4_22_2","doi-asserted-by":"publisher","DOI":"10.1038\/nbt.3300"},{"key":"e_1_3_4_23_2","unstructured":"Kim Y. 2015. Convolutional neural networks for sentence classification. Master\u2019s thesis University of Waterloo"},{"key":"e_1_3_4_24_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41588-018-0295-5"},{"key":"e_1_3_4_25_2","doi-asserted-by":"publisher","unstructured":"Gudavarthi T Reddy Seri SV Thadoju AC Sarangi PK Jabbar MA. 2024. DNA sequence classification using RRCNN and LSTM. doi:10.1109\/ICDCOT61034.2024.10515792","DOI":"10.1109\/ICDCOT61034.2024.10515792"},{"key":"e_1_3_4_26_2","doi-asserted-by":"publisher","unstructured":"Vaswani A Shazeer NM Parmar N Uszkoreit J Jones L Gomez AN Kaiser L Polosukhin I. 2017. Attention is all you need. arXiv. doi:10.48550\/arXiv.1706.03762","DOI":"10.48550\/arXiv.1706.03762"},{"key":"e_1_3_4_27_2","doi-asserted-by":"publisher","unstructured":"Zhou Z Ji Y Li W Dutta P Davuluri R Liu H. 2023. DNABERT: efficient foundation model and benchmark for multi-species genome. arXiv. doi:10.48550\/arXiv.2306.15006.","DOI":"10.48550\/arXiv.2306.15006"},{"key":"e_1_3_4_28_2","doi-asserted-by":"publisher","unstructured":"Kudo T Richardson J Assoc Computat L. 2018. SentencePiece: a simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv. doi:10.48550\/arXiv.1808.06226","DOI":"10.48550\/arXiv.1808.06226"},{"key":"e_1_3_4_29_2","doi-asserted-by":"publisher","unstructured":"Sennrich R Haddow B Birch A. 2016. Neural machine translation of rare words with subword units. arXiv. doi:10.48550\/arXiv.1508.07909","DOI":"10.48550\/arXiv.1508.07909"},{"key":"e_1_3_4_30_2","doi-asserted-by":"publisher","DOI":"10.3390\/antibiotics11111611"},{"key":"e_1_3_4_31_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-021-01619-9"},{"key":"e_1_3_4_32_2","doi-asserted-by":"publisher","DOI":"10.1186\/s40168-018-0401-z"},{"key":"e_1_3_4_33_2","doi-asserted-by":"publisher","DOI":"10.1128\/spectrum.01919-22"}],"container-title":["Microbiology Spectrum"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.asm.org\/doi\/pdf\/10.1128\/spectrum.01906-25","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.asm.org\/doi\/pdf\/10.1128\/spectrum.01906-25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T14:40:46Z","timestamp":1786027246000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.asm.org\/doi\/10.1128\/spectrum.01906-25"}},"subtitle":[],"editor":[{"given":"Kai","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,8,4]]},"references-count":32,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2026,8,4]]}},"alternative-id":["10.1128\/spectrum.01906-25"],"URL":"https:\/\/doi.org\/10.1128\/spectrum.01906-25","relation":{},"ISSN":["2165-0497"],"issn-type":[{"value":"2165-0497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,4]]},"assertion":[{"value":"2025-06-25","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-12-22","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-07-15","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e01906-25"}}