ReviewBriefings in bioinformatics2023
Concepts and methods for transcriptome-wide prediction of chemical messenger RNA modifications with machine learning.
Review in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- RNA Aptamers and Epitranscriptomics: Charting Unexplored Territories in RNA Biology.Molecular diagnosis & therapy · 2026Review
- Gaining insights into epigenetic memories through artificial intelligence and omics science in plants.Journal of integrative plant biology · 2025Review
- MCAMEF-BERT: an efficient deep learning method for RNA N7-methylguanosine site prediction via multi-branch feature integration.Briefings in bioinformatics · 2025Article
- Multimodal zero-shot learning of previously unseen epitranscriptomes from RNA-seq data.Briefings in bioinformatics · 2025Article
- RNA epigenetic modifications as dynamic biomarkers in cancer: from mechanisms to clinical translation.Biomarker research · 2025Review
- Statistical modeling of single-cell epitranscriptomics enabled trajectory and regulatory inference of RNA methylation.Cell genomics · 2025Article
- Detecting a wide range of epitranscriptomic modifications using a nanopore-sequencing-based computational approach with 1D score-clustering.Nucleic acids research · 2025Article
- Epigenetic orchestration of RNA mInternational journal of biological sciences · 2025Review
- SARS-CoV-2 Displays a Suboptimal Codon Usage Bias for Efficient Translation in Human Cells Diverted by Hijacking the tRNA Epitranscriptome.International journal of molecular sciences · 2024Article
- Big data and deep learning for RNA biology.Experimental & molecular medicine · 2024Review
- Detection and Quantification of 5moU RNA Modification from Direct RNA Sequencing Data.Current genomics · 2024Article
- Benchmarking of computational methods for m6A profiling with Nanopore direct RNA sequencing.Briefings in bioinformatics · 2024Article
- The rise of epitranscriptomics: recent developments and future directions.Trends in pharmacological sciences · 2024Review
- 'Artificial intelligence and machine learning in RNA biology'.Briefings in bioinformatics · 2023Article
- A primer for junior trainees: Recognition of RNA modifications by RNA-binding proteins.Biochemistry and molecular biology education : a bimonthly publication of the International Union of Biochemistry and Molecular BiologyReview
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The expanding field of epitranscriptomics might rival the epigenome in the diversity of biological processes impacted. In recent years, the development of new high-throughput experimental and computational techniques has been a key driving force in discovering the properties of RNA modifications. Machine learning applications, such as for classification, clustering or de novo identification, have been critical in these advances. Nonetheless, various challenges remain before the full potential of machine learning for epitranscriptomics can be leveraged. In this review, we provide a comprehensive survey of machine learning methods to detect RNA modifications using diverse input data sources. We describe strategies to train and test machine learning methods and to encode and interpret features that are relevant for epitranscriptomics. Finally, we identify some of the current challenges and open questions about RNA modification analysis, including the ambiguity in predicting RNA modifications in transcript isoforms or in single nucleotides, or the lack of complete ground truth sets to test RNA modifications. We believe this review will inspire and benefit the rapidly developing field of epitranscriptomics in addressing the current limitations through the effective use of machine learning.
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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.