ArticleBMC biology2023
m5U-SVM: identification of RNA 5-methyluridine modification sites based on multi-view features of physicochemical features and distributed representation.
Article in BMC biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Deciphering RNA modification and post-transcriptional regulation with NetRNApan.Briefings in bioinformatics · 2025Article
- A robust deep learning framework for RNA 5-methyluridine modification prediction using integrated features.BMC biology · 2025Article
- Review
- Classification of Acid and Alkaline Enzymes Based on Normalized Van der Waals Volume Features.Proteomics. Clinical applications · 2025Article
- EDS-Kcr: deep supervision based on large language model for identifying protein lysine crotonylation sites across multiple species.Briefings in bioinformatics · 2025Article
- Excavation of gene markers associated with pancreatic ductal adenocarcinoma based on interrelationships of gene expression.IET systems biology · 2024Article
- Deep-m5U: a deep learning-based approach for RNA 5-methyluridine modification prediction using optimized feature integration.BMC bioinformatics · 2024Article
- Identifying nucleotide-binding leucine-rich repeat receptor and pathogen effector pairing using transfer-learning and bilinear attention network.Bioinformatics (Oxford, England) · 2024Article
- Bioinformatics for Inosine: Tools and Approaches to Trace This Elusive RNA Modification.Genes · 2024Review
- ac4C-AFL: A high-precision identification of human mRNA N4-acetylcytidine sites based on adaptive feature representation learning.Molecular therapy. Nucleic acids · 2024Article
- msBERT-Promoter: a multi-scale ensemble predictor based on BERT pre-trained model for the two-stage prediction of DNA promoters and their strengths.BMC biology · 2024Article
- Prediction of blood-brain barrier penetrating peptides based on data augmentation with Augur.BMC biology · 2024Article
- Fuzzy kernel evidence Random Forest for identifying pseudouridine sites.Briefings in bioinformatics · 2024Article
- Article
- A computational model of circRNA-associated diseases based on a graph neural network: prediction and case studies for follow-up experimental validation.BMC biology · 2024Article
- FEOpti-ACVP: identification of novel anti-coronavirus peptide sequences based on feature engineering and optimization.Briefings in bioinformatics · 2024Article
- Identification, characterization and expression analysis of circRNA encoded by SARS-CoV-1 and SARS-CoV-2.Briefings in bioinformatics · 2024Article
- DisoFLAG: accurate prediction of protein intrinsic disorder and its functions using graph-based interaction protein language model.BMC biology · 2024Article
- Accurately identifying hemagglutinin using sequence information and machine learning methods.Frontiers in medicine · 2023Article
- m5U-GEPred: prediction of RNA 5-methyluridine sites based on sequence-derived and graph embedding features.Frontiers in microbiology · 2023Article
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5 authors.
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Abstract
backgroundRNA 5-methyluridine (m5U) modifications are obtained by methylation at the C
resultsIn this study, we have developed a novel predictor, m5U-SVM, based on multi-view features and machine learning algorithms to construct predictive models for identifying m5U modification sites from RNA sequences. In this method, we used four traditional physicochemical features and distributed representation features. The optimized multi-view features were obtained from the four fused traditional physicochemical features by using the two-step LightGBM and IFS methods, and then the distributed representation features were fused with the optimized physicochemical features to obtain the new multi-view features. The best performing classifier, support vector machine, was identified by screening different machine learning algorithms. Compared with the results, the performance of the proposed model is better than that of the existing state-of-the-art tool.
conclusionsm5U-SVM provides an effective tool that successfully captures sequence-related attributes of modifications and can accurately predict m5U modification sites from RNA sequences. The identification of m5U modification sites helps to understand and delve into the related biological processes and functions.
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