ArticleComputational and structural biotechnology journal2020
Accurate prediction of RNA 5-hydroxymethylcytosine modification by utilizing novel position-specific gapped k-mer descriptors.
Article in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 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
- A hybrid residue based sequential encoding mechanism with XGBoost improved ensemble model for identifying 5-hydroxymethylcytosine modifications.Scientific reports · 2024Article
- Deep5hmC: predicting genome-wide 5-hydroxymethylcytosine landscape via a multimodal deep learning model.Bioinformatics (Oxford, England) · 2024Article
- Sequence based model using deep neural network and hybrid features for identification of 5-hydroxymethylcytosine modification.Scientific reports · 2024Article
- Deep5hmC: Predicting genome-wide 5-Hydroxymethylcytosine landscape via a multimodal deep learning model.bioRxiv : the preprint server for biology · 2024Article
- Emerging Role of Environmental Epitranscriptomics and RNA Modifications in Parkinson's Disease.Journal of Parkinson's disease · 2024Review
- CNN6mA: Interpretable neural network model based on position-specific CNN and cross-interactive network for 6mA site prediction.Computational and structural biotechnology journal · 2023Article
- i5hmCVec: Identifying 5-Hydroxymethylcytosine Sites ofFrontiers in genetics · 2022Article
- Dynamic regulation and key roles of ribonucleic acid methylation.Frontiers in cellular neuroscience · 2022Review
- MILNP: Plant lncRNA-miRNA Interaction Prediction Based on Improved Linear Neighborhood Similarity and Label Propagation.Frontiers in plant science · 2022Article
- Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data.Bioinformatics (Oxford, England) · 2021Article
- Review
- Machine learning applications in RNA modification sites prediction.Computational and structural biotechnology journal · 2021Review
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7 authors.
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Abstract
RNA modification is an essential step towards generation of new RNA structures. Such modification is potentially able to modify RNA function or its stability. Among different modifications, 5-Hydroxymethylcytosine (5hmC) modification of RNA exhibit significant potential for a series of biological processes. Understanding the distribution of 5hmC in RNA is essential to determine its biological functionality. Although conventional sequencing techniques allow broad identification of 5hmC, they are both time-consuming and resource-intensive. In this study, we propose a new computational tool called iRNA5hmC-PS to tackle this problem. To build iRNA5hmC-PS we extract a set of novel sequence-based features called Position-Specific Gapped k-mer (PSG k-mer) to obtain maximum sequential information. Our feature analysis shows that our proposed PSG k-mer features contain vital information for the identification of 5hmC sites. We also use a group-wise feature importance calculation strategy to select a small subset of features containing maximum discriminative information. Our experimental results demonstrate that iRNA5hmC-PS is able to enhance the prediction performance, dramatically. iRNA5hmC-PS achieves 78.3% prediction performance, which is 12.8% better than those reported in the previous studies. iRNA5hmC-PS is publicly available as an online tool at http://103.109.52.8:81/iRNA5hmC-PS. Its benchmark dataset, source codes, and documentation are available at https://github.com/zahid6454/iRNA5hmC-PS.
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