ArticleFrontiers in bioengineering and biotechnology2020
iRNA5hmC: The First Predictor to Identify RNA 5-Hydroxymethylcytosine Modifications Using Machine Learning.
Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Deep learning based prediction of RNA 5hmC modifications using composite feature representations and comparative benchmarking with transformer models.BioData mining · 2026Article
- Article
- RNA modifications and their role in gene expression.Frontiers in molecular biosciences · 2025Review
- A hybrid residue based sequential encoding mechanism with XGBoost improved ensemble model for identifying 5-hydroxymethylcytosine modifications.Scientific reports · 2024Article
- DeepPGD: A Deep Learning Model for DNA Methylation Prediction Using Temporal Convolution, BiLSTM, and Attention Mechanism.International journal of molecular sciences · 2024Article
- i5mC-DCGA: an improved hybrid network framework based on the CBAM attention mechanism for identifying promoter 5mC sites.BMC genomics · 2024Article
- Exploring the epigenetic landscape: The role of 5-hydroxymethylcytosine in neurodevelopmental disorders.Cambridge prisms. Precision medicine · 2024Review
- RNADSN: Transfer-Learning 5-Methyluridine (mInternational journal of molecular sciences · 2022Article
- i5hmCVec: Identifying 5-Hydroxymethylcytosine Sites ofFrontiers in genetics · 2022Article
- Dynamic regulation and key roles of ribonucleic acid methylation.Frontiers in cellular neuroscience · 2022Review
- Epigenetics: Roles and therapeutic implications of non-coding RNA modifications in human cancers.Molecular therapy. Nucleic acids · 2021Review
- iBitter-Fuse: A Novel Sequence-Based Bitter Peptide Predictor by Fusing Multi-View Features.International journal of molecular sciences · 2021Article
- Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data.Bioinformatics (Oxford, England) · 2021Article
- Research on the Computational Prediction of Essential Genes.Frontiers in cell and developmental biology · 2021Review
- Machine learning applications in RNA modification sites prediction.Computational and structural biotechnology journal · 2021Review
- Accurate prediction of RNA 5-hydroxymethylcytosine modification by utilizing novel position-specific gapped k-mer descriptors.Computational and structural biotechnology journal · 2020Article
- Bioinformatics approaches for deciphering the epitranscriptome: Recent progress and emerging topics.Computational and structural biotechnology journal · 2020Review
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5 authors.
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Abstract
RNA 5-hydroxymethylcytosine (5hmC) modification plays an important role in a series of biological processes. Characterization of its distributions in transcriptome is fundamentally important to reveal the biological functions of 5hmC. Sequencing-based technologies allow the high-throughput identification of 5hmC; however, they are labor-intensive, time-consuming, as well as expensive. Thus, there is an urgent need to develop more effective and efficient computational methods, at least complementary to the high-throughput technologies. In this study, we developed iRNA5hmC, a computational predictive protocol to identify RNA 5hmC sites using machine learning. In this predictor, we introduced a sequence-based feature algorithm consisting of two feature representations, (1)
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