ArticleMolecular therapy. Nucleic acids2019
iPseU-CNN: Identifying RNA Pseudouridine Sites Using Convolutional Neural Networks.
Article in Molecular therapy. Nucleic acids, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed, 96 citations in OpenAlex.
- Fusion_f5C-Pred: a dual-branch feature fusion framework for 5-formylcytosine modification sites prediction.BMC genomics · 2025Article
- A deep learning framework for lysine 2-hydroxyisobutyrylation site prediction using evolutionary feature representation.Scientific reports · 2025Article
- Block sparse Bayes-based fuzzy system for RNA N6-methyladenosine sites prediction.PLoS computational biology · 2025Article
- RSCNN-PseU: random searching-based convolutional neural network model for identifying RNA pseudouridine.Briefings in bioinformatics · 2025Article
- Definer: A computational method for accurate identification of RNA pseudouridine sites based on deep learning.PloS one · 2025Article
- GBMPhos: A Gating Mechanism and Bi-GRU-Based Method for Identifying Phosphorylation Sites of SARS-CoV-2 Infection.Biology · 2024Article
- Bioinformatics for Inosine: Tools and Approaches to Trace This Elusive RNA Modification.Genes · 2024Review
- Big data and deep learning for RNA biology.Experimental & molecular medicine · 2024Review
- Article
- Fuzzy kernel evidence Random Forest for identifying pseudouridine sites.Briefings in bioinformatics · 2024Article
- Interpretable Multi-Scale Deep Learning for RNA Methylation Analysis across Multiple Species.International journal of molecular sciences · 2024Article
- A CNN based m5c RNA methylation predictor.Scientific reports · 2023Article
- PseU-ST: A new stacked ensemble-learning method for identifying RNA pseudouridine sites.Frontiers in genetics · 2023Article
- Evaluation and development of deep neural networks for RNA 5-Methyluridine classifications using autoBioSeqpy.Frontiers in microbiology · 2023Article
- Predicting Pseudouridine Sites with Porpoise.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Predicting N6-Methyladenosine Sites in Multiple Tissues of Mammals through Ensemble Deep Learning.International journal of molecular sciences · 2022Review
- Penguin: A tool for predicting pseudouridine sites in direct RNA nanopore sequencing data.Methods (San Diego, Calif.) · 2022Article
- Dynamic regulation and key roles of ribonucleic acid methylation.Frontiers in cellular neuroscience · 2022Review
- Identification of piRNA disease associations using deep learning.Computational and structural biotechnology journal · 2022Article
- Biological Sequence Classification: A Review on Data and General Methods.Research (Washington, D.C.) · 2022Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pseudouridine is the most prevalent RNA modification and has been found in both eukaryotes and prokaryotes. Currently, pseudouridine has been demonstrated in several kinds of RNAs, such as small nuclear RNA, rRNA, tRNA, mRNA, and small nucleolar RNA. Therefore, its significance to academic research and drug development is understandable. Through biochemical experiments, the pseudouridine site identification has produced good outcomes, but these lab exploratory methods and biochemical processes are expensive and time consuming. Therefore, it is important to introduce efficient methods for identification of pseudouridine sites. In this study, an intelligent method for pseudouridine sites using the deep-learning approach was developed. The proposed prediction model is called iPseU-CNN (identifying pseudouridine by convolutional neural networks). The existing methods used handcrafted features and machine-learning approaches to identify pseudouridine sites. However, the proposed predictor extracts the features of the pseudouridine sites automatically using a convolution neural network model. The iPseU-CNN model yields better outcomes than the current state-of-the-art models in all evaluation parameters. It is thus highly projected that the iPseU-CNN predictor will become a helpful tool for academic research on pseudouridine site prediction of RNA, as well as in drug discovery.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.