{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T16:46:21Z","timestamp":1783097181825,"version":"3.54.6"},"reference-count":45,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2024,2,25]],"date-time":"2024-02-25T00:00:00Z","timestamp":1708819200000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Yunnan Provincial Foundation for Leaders of Disciplines in Science and Technology","award":["202305AC160014"],"award-info":[{"award-number":["202305AC160014"]}]},{"name":"Innovation Research Foundation for Graduate Students of Yunnan University","award":["KC-22221489"],"award-info":[{"award-number":["KC-22221489"]}]},{"name":"Research Project of Yunnan Province\u2014Youth Project","award":["202001AU070002"],"award-info":[{"award-number":["202001AU070002"]}]},{"name":"Yunnan Police College","award":["19A009"],"award-info":[{"award-number":["19A009"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,3,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Liquid chromatography retention times prediction can assist in metabolite identification, which is a critical task and challenge in nontargeted metabolomics. However, different chromatographic conditions may result in different retention times for the same metabolite. Current retention time prediction methods lack sufficient scalability to transfer from one specific chromatographic method to another.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Therefore, we present RT-Transformer, a novel deep neural network model coupled with graph attention network and 1D-Transformer, which can predict retention times under any chromatographic methods. First, we obtain a pre-trained model by training RT-Transformer on the large small molecule retention time dataset containing 80\u00a0038 molecules, and then transfer the resulting model to different chromatographic methods based on transfer learning. When tested on the small molecule retention time dataset, as other authors did, the average absolute error reached 27.30 after removing not retained molecules. Still, it reached 33.41 when no samples were removed. The pre-trained RT-Transformer was further transferred to 5 datasets corresponding to different chromatographic conditions and fine-tuned. According to the experimental results, RT-Transformer achieves competitive performance compared to state-of-the-art methods. In addition, RT-Transformer was applied to 41 external molecular retention time datasets. Extensive evaluations indicate that RT-Transformer has excellent scalability in predicting retention times for liquid chromatography and improves the accuracy of metabolite identification.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code for the model is available at https:\/\/github.com\/01dadada\/RT-Transformer. The web server is available at https:\/\/huggingface.co\/spaces\/Xue-Jun\/RT-Transformer.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae084","type":"journal-article","created":{"date-parts":[[2024,2,25]],"date-time":"2024-02-25T11:35:35Z","timestamp":1708860935000},"source":"Crossref","is-referenced-by-count":44,"title":["RT-Transformer: retention time prediction for metabolite annotation to assist in metabolite identification"],"prefix":"10.1093","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-0496-8447","authenticated-orcid":false,"given":"Jun","family":"Xue","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Yunnan University , Kunming, Yunnan 650500, China"},{"name":"Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences , Shenzhen, Guangdong 518120, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bingyi","family":"Wang","sequence":"additional","affiliation":[{"name":"Yunnan Police College , Kunming, Yunnan 650223, China"},{"name":"Key Laboratory of Smart Drugs Control (Yunnan Police College), Ministry of Education , Kunming, Yunnan 650223, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongchao","family":"Ji","sequence":"additional","affiliation":[{"name":"Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences , Shenzhen, Guangdong 518120, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9060-382X","authenticated-orcid":false,"given":"WeiHua","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information 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