Li, Zhirou, Mao, Jinge, Huang, Daiyun, Song, Bowen and Meng, Jia 
ORCID: 0000-0003-3455-205X
(2022)
RNADSN: Transfer-Learning 5-Methyluridine (m<SUP>5</SUP>U) Modification on mRNAs from Common Features of tRNA.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 23 (21).
13493-.
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RNADSN Transfer-Learning 5-Methyluridine (msup5supU) Modification on mRNAs from Common Features of tRNA.pdf - Open Access published version Download (846kB) | Preview |
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 achieved another distinct improvement and presented an average area under the receiver operating characteristic curve (AUC) of 0.9422 and an average precision (AP) of 0.7855. The robust performance of RNADSN was also verified by cross-technical and cross-cellular validation. The interpretation of RNADSN also revealed the sequence motif of common features. Therefore, RNADSN should be a useful tool for studying m<sup>5</sup>U modification.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | 5-methyluridine, deep neural network, transfer learning, RNA modification, site prediction |
| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 06 Jul 2023 08:27 |
| Last Modified: | 18 Oct 2023 12:29 |
| DOI: | 10.3390/ijms232113493 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3171444 |
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