{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T02:26:57Z","timestamp":1775788017859,"version":"3.50.1"},"reference-count":63,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T00:00:00Z","timestamp":1718582400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31970635"],"award-info":[{"award-number":["31970635"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>ADP-ribosylation is a critical modification involved in regulating diverse cellular processes, including chromatin structure regulation, RNA transcription, and cell death. Bacterial ADP-ribosyltransferase toxins (bARTTs) serve as potent virulence factors that orchestrate the manipulation of host cell functions to facilitate bacterial pathogenesis. Despite their pivotal role, the bioinformatic identification of novel bARTTs poses a formidable challenge due to limited verified data and the inherent sequence diversity among bARTT members.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We proposed a deep learning-based model, ARTNet, specifically engineered to predict bARTTs from bacterial genomes. Initially, we introduced an effective data augmentation method to address the issue of data scarcity in training ARTNet. Subsequently, we employed a data optimization strategy by utilizing ART-related domain subsequences instead of the primary full sequences, thereby significantly enhancing the performance of ARTNet. ARTNet achieved a Matthew\u2019s correlation coefficient (MCC) of 0.9351 and an F1-score (macro) of 0.9666 on repeated independent test datasets, outperforming three other deep learning models and six traditional machine learning models in terms of time efficiency and accuracy. Furthermore, we empirically demonstrated the ability of ARTNet to predict novel bARTTs across domain superfamilies without sequence similarity. We anticipate that ARTNet will greatly facilitate the screening and identification of novel bARTTs from bacterial genomes.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>ARTNet is publicly accessible at http:\/\/www.mgc.ac.cn\/ARTNet\/. The source code of ARTNet is freely available at https:\/\/github.com\/zhengdd0422\/ARTNet\/.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae378","type":"journal-article","created":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T20:58:24Z","timestamp":1718657904000},"source":"Crossref","is-referenced-by-count":1,"title":["A deep learning method to predict bacterial ADP-ribosyltransferase toxins"],"prefix":"10.1093","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3025-3557","authenticated-orcid":false,"given":"Dandan","family":"Zheng","sequence":"first","affiliation":[{"name":"NHC Key Laboratory of Systems Biology of Pathogens, National Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing 102629, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siyu","family":"Zhou","sequence":"additional","affiliation":[{"name":"NHC Key Laboratory of Systems Biology of Pathogens, National Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing 102629, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lihong","family":"Chen","sequence":"additional","affiliation":[{"name":"NHC Key Laboratory of Systems Biology of Pathogens, National Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing 102629, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guansong","family":"Pang","sequence":"additional","affiliation":[{"name":"School of Computing and Information Systems, Singapore Management University , Singapore 178902, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8826-5198","authenticated-orcid":false,"given":"Jian","family":"Yang","sequence":"additional","affiliation":[{"name":"NHC Key Laboratory of Systems Biology of Pathogens, National Institute of 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