{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T09:15:06Z","timestamp":1774689306172,"version":"3.50.1"},"reference-count":47,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2025,7,29]],"date-time":"2025-07-29T00:00:00Z","timestamp":1753747200000},"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":["62225209"],"award-info":[{"award-number":["62225209"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62472445"],"award-info":[{"award-number":["62472445"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>2\u2032-O-methylation (2OMe) is a common post-transcriptional modification in RNA that plays a crucial role in regulating gene expression and is implicated in various biological processes and diseases. Computational methods offer an efficient alternative to the time-consuming and costly experimental identification of 2OMe sites. Recent advancements in RNA pre-trained language models have revolutionized RNA bioinformatics. However, there remains a gap in their application specifically for predicting 2OMe sites.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In the study, we propose a novel deep learning framework, 2OMe-LM, for predicting 2OMe sites in RNA. 2OMe-LM integrates RNA sequence features derived from RNA pre-trained language models with those obtained from the word2vec technique. Then, 2OMe-LM employs fully connected layers and a bidirectional long short-term memory network to process the two types of features separately, followed by a feature fusion module for the final prediction. Additionally, an attention block is incorporated to provide the interpretability of the prediction results. The results demonstrate that 2OMe-LM significantly outperforms existing state-of-the-art predictors, with features from RNA pre-trained language models proving to be critical. Motif analysis further demonstrates 2OMe-LM\u2019s potential for discovering 2OMe-related motifs.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The 2OMe-LM web server is available at https:\/\/csuligroup.com:9200\/2OMe-LM. The source code can be obtained from https:\/\/github.com\/CSUBioGroup\/2OMe-LM.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf417","type":"journal-article","created":{"date-parts":[[2025,7,29]],"date-time":"2025-07-29T16:33:10Z","timestamp":1753806790000},"source":"Crossref","is-referenced-by-count":3,"title":["2OMe-LM: predicting 2\u2032-O-methylation sites in human RNA using a pre-trained RNA language model"],"prefix":"10.1093","volume":"41","author":[{"given":"Qianpei","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , Changsha 410083,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1726-0955","authenticated-orcid":false,"given":"Min","family":"Zeng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , Changsha 410083,","place":["China"]}]},{"given":"Yiming","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , Changsha 410083,","place":["China"]}]},{"given":"Chengqian","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Key Laboratory of Intelligent Computing and Information Processing, Xiangtan University , Xiangtan, Hunan 411105,","place":["China"]}]},{"given":"Shichao","family":"Kan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , Changsha 410083,","place":["China"]}]},{"given":"Fei","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , Changsha 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