ReviewComputational and structural biotechnology journal2021
Machine learning applications in RNA modification sites prediction.
Review in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Computational prediction of RNA modification site-disease associations: a systematic review.Briefings in bioinformatics · 2026Pooled it
- Accelerate the Highly Efficient Development of mRNA Vaccines Through Advanced Computational Methods.MedComm · 2026Review
- Single-cell and machine learning approaches reveal METTL14-mediated autophagy via PI3K/AKT signaling in invasive PitNET.Cancer cell international · 2026Article
- A robust deep learning framework for RNA 5-methyluridine modification prediction using integrated features.BMC biology · 2025Article
- MCAMEF-BERT: an efficient deep learning method for RNA N7-methylguanosine site prediction via multi-branch feature integration.Briefings in bioinformatics · 2025Article
- 2OM-Pred: prediction of 2-O-methylation sites in ribonucleic acid using diverse classifiers.Briefings in bioinformatics · 2025Article
- RNA modifications and their role in gene expression.Frontiers in molecular biosciences · 2025Review
- RNA-ModX: a multilabel prediction and interpretation framework for RNA modifications.Briefings in bioinformatics · 2024Article
- Deep-m5U: a deep learning-based approach for RNA 5-methyluridine modification prediction using optimized feature integration.BMC bioinformatics · 2024Article
- A robust deep learning approach for identification of RNA 5-methyluridine sites.Scientific reports · 2024Article
- Deep learning modeling of RNA ac4C deposition reveals the importance of plant alternative splicing.Plant molecular biology · 2024Article
- TransPTM: a transformer-based model for non-histone acetylation site prediction.Briefings in bioinformatics · 2024Article
- MSCAN: multi-scale self- and cross-attention network for RNA methylation site prediction.BMC bioinformatics · 2024Article
- Analysis approaches for the identification and prediction ofEpigenetics · 2023Review
- Concepts and methods for transcriptome-wide prediction of chemical messenger RNA modifications with machine learning.Briefings in bioinformatics · 2023Review
- 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
- EMDL-ac4C: identifying N4-acetylcytidine based on ensemble two-branch residual connection DenseNet and attention.Frontiers in genetics · 2023Article
- Heuristic Analysis of Genomic Sequence Processing Models for High Efficiency Prediction: A Statistical Perspective.Current genomics · 2022Review
- Geographic encoding of transcripts enabled high-accuracy and isoform-aware deep learning of RNA methylation.Nucleic acids research · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ribonucleic acid (RNA) modifications are post-transcriptional chemical composition changes that have a fundamental role in regulating the main aspect of RNA function. Recently, large datasets have become available thanks to the recent development in deep sequencing and large-scale profiling. This availability of transcriptomic datasets has led to increased use of machine learning based approaches in epitranscriptomics, particularly in identifying RNA modifications. In this review, we comprehensively explore machine learning based approaches used for the prediction of 11 RNA modification types, namely,
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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.