{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Li Z"],"funding":["XJTLU Key Program Special Fund","National Natural Science Foundation of China"],"pagination":["13493"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9655583"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["23(21)"],"pubmed_abstract":["One of the most abundant non-canonical bases widely occurring on various RNA molecules is 5-methyluridine (m5U). Recent studies have revealed its influences on the development of breast cancer, systemic lupus erythematosus, and the regulation of stress responses. The accurate identification of m<sup>5</sup>U sites is crucial for understanding their biological functions. We propose RNADSN, the first transfer learning deep neural network that learns common features between tRNA m<sup>5</sup>U and mRNA m<sup>5</sup>U to enhance the prediction of mRNA m<sup>5</sup>U. Without seeing the experimentally detected mRNA m<sup>5</sup>U sites, RNADSN has already outperformed the state-of-the-art method, m5UPred. Using mRNA m<sup>5</sup>U classification as an additional layer of supervision, our model "],"journal":["International journal of molecular sciences"],"pubmed_title":["RNADSN: Transfer-Learning 5-Methyluridine (m<sup>5</sup>U) Modification on mRNAs from Common Features of tRNA."],"pmcid":["PMC9655583"],"funding_grant_id":["KSF-P-02","32100519","KSF-E-51","31671373"],"pubmed_authors":["Li Z","Huang D","Song B","Meng J","Mao J"],"additional_accession":[]},"is_claimable":false,"name":"RNADSN: Transfer-Learning 5-Methyluridine (m<sup>5</sup>U) Modification on mRNAs from Common Features of tRNA.","description":"One of the most abundant non-canonical bases widely occurring on various RNA molecules is 5-methyluridine (m5U). Recent studies have revealed its influences on the development of breast cancer, systemic lupus erythematosus, and the regulation of stress responses. The accurate identification of m<sup>5</sup>U sites is crucial for understanding their biological functions. We propose RNADSN, the first transfer learning deep neural network that learns common features between tRNA m<sup>5</sup>U and mRNA m<sup>5</sup>U to enhance the prediction of mRNA m<sup>5</sup>U. Without seeing the experimentally detected mRNA m<sup>5</sup>U sites, RNADSN has already outperformed the state-of-the-art method, m5UPred. Using mRNA m<sup>5</sup>U classification as an additional layer of supervision, our model ","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Nov","modification":"2025-04-04T13:30:21.509Z","creation":"2024-12-04T03:13:00.53Z"},"accession":"S-EPMC9655583","cross_references":{"pubmed":["36362279"],"doi":["10.3390/ijms232113493"]}}