Academic Journal
Feadm5C: Enhancing prediction of RNA 5-Methylcytosine modification sites with physicochemical molecular graph features.
| Τίτλος: | Feadm5C: Enhancing prediction of RNA 5-Methylcytosine modification sites with physicochemical molecular graph features. |
|---|---|
| Συγγραφείς: | Jiang D; School of Computer Science and Technology, Xidian University, Xi'an 710071, Shaanxi, China., Ao C; School of Computer Science and Technology, Xidian University, Xi'an 710071, Shaanxi, China., Li Y; School of Management, Xi'an Polytechnic University, Xi'an 710000, Shaanxi, China., Yu L; School of Computer Science and Technology, Xidian University, Xi'an 710071, Shaanxi, China. Electronic address: lyu@xidian.edu.cn. |
| Πηγή: | Genomics [Genomics] 2025 May; Vol. 117 (3), pp. 111037. Date of Electronic Publication: 2025 Mar 22. |
| Τύπος έκδοσης: | Journal Article; Research Support, Non-U.S. Gov't |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Academic Press Country of Publication: United States NLM ID: 8800135 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1089-8646 (Electronic) Linking ISSN: 08887543 NLM ISO Abbreviation: Genomics Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: San Diego : Academic Press, [c1987- |
| Ιατρικοί όροι (MeSH): | 5-Methylcytosine*/metabolism , 5-Methylcytosine*/chemistry , RNA*/chemistry , RNA*/metabolism , RNA*/genetics , RNA Processing, Post-Transcriptional* , Software*, Sequence Analysis, RNA/methods ; Machine Learning |
| Περίληψη: | One common post-transcriptional modification that is essential to biological activities is RNA 5-methylcytosine (m5C). A large amount of RNA data containing m5C modification sites has been gathered as a result of the rapid development of high-throughput sequencing technology. While there are a lot of machine learning based techniques available for identifying m5C alteration sites, these models' accuracy still has to be raised. This study proposed a novel method, Feadm5C, which predicts m5C based on fusing molecular graph features and sequencing information together. 10-fold cross-validation was used to assess the model's predictive performance. In addition, we used t-SNE visualization to assess the model's stability and effectiveness. While keeping feature encoding and model structure straightforward, the approach suggested in this work outperforms the most recent approaches in use. The dataset and code of the model can be downloaded from GitHub (https://github.com/LiangYu-Xidian/Feadm5C). (Copyright © 2025. Published by Elsevier Inc.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: BiLSTM; Deep learning; Molecular graph feature; RNA 5-methylcytosine |
| Substance Nomenclature: | 6R795CQT4H (5-Methylcytosine) 63231-63-0 (RNA) |
| Entry Date(s): | Date Created: 20250324 Date Completed: 20250525 Latest Revision: 20260517 |
| Update Code: | 20260518 |
| DOI: | 10.1016/j.ygeno.2025.111037 |
| PMID: | 40127825 |
| Βάση Δεδομένων: | MEDLINE |
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