ArticleOncotarget2017
iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition.
Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.
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Who cites it
62 citing papers in PubMed, 214 citations in OpenAlex.
- Domain-derived knowledge enabled machine learning and functional characterization of cancer-associated RNA methylation sites.BMC bioinformatics · 2026Article
- Detection, molecular function and mechanisms of m5C in cancer.Clinical and translational medicine · 2025Review
- FusDRM-m5C: a hybrid model for accurate prediction of 5-methylcytosine modification sites based on feature fusion and attention mechanism.Frontiers in genetics · 2025Article
- m5CStack: An integrated framework for m5C site prediction using multi-feature stacking.Computational and structural biotechnology journal · 2025Article
- Definer: A computational method for accurate identification of RNA pseudouridine sites based on deep learning.PloS one · 2025Article
- Domain-knowledge enabled ensemble learning of 5-formylcytosine (f5C) modification sites.Computational and structural biotechnology journal · 2024Article
- Bioinformatics for Inosine: Tools and Approaches to Trace This Elusive RNA Modification.Genes · 2024Review
- m6A-TCPred: a web server to predict tissue-conserved human mBMC bioinformatics · 2024Article
- ASRpro: A machine-learning computational model for identifying proteins associated with multiple abiotic stress in plants.The plant genome · 2024Article
- A CNN based m5c RNA methylation predictor.Scientific reports · 2023Article
- XGBoost framework with feature selection for the prediction of RNA N5-methylcytosine sites.Molecular therapy : the journal of the American Society of Gene Therapy · 2023Article
- m6A-TSHub: Unveiling the Context-specific mGenomics, proteomics & bioinformatics · 2023Article
- m5U-GEPred: prediction of RNA 5-methyluridine sites based on sequence-derived and graph embedding features.Frontiers in microbiology · 2023Article
- Geographic encoding of transcripts enabled high-accuracy and isoform-aware deep learning of RNA methylation.Nucleic acids research · 2022Article
- Review
- Deepm5C: A deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy.Molecular therapy : the journal of the American Society of Gene Therapy · 2022Article
- NSUN2-mediated RNA mEpigenetics · 2022Article
- Roles of Physicochemical and Structural Properties of RNA-Binding Proteins in Predicting the Activities of Trans-Acting Splicing Factors with Machine Learning.International journal of molecular sciences · 2022Article
- Dynamic regulation and key roles of ribonucleic acid methylation.Frontiers in cellular neuroscience · 2022Review
- m5Cpred-XS: A New Method for Predicting RNA m5C Sites Based on XGBoost and SHAP.Frontiers in genetics · 2022Article
2 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Occurring at cytosine (C) of RNA, 5-methylcytosine (m5C) is an important post-transcriptional modification (PTCM). The modification plays significant roles in biological processes by regulating RNA metabolism in both eukaryotes and prokaryotes. It may also, however, cause cancers and other major diseases. Given an uncharacterized RNA sequence that contains many C residues, can we identify which one of them can be of m5C modification, and which one cannot? It is no doubt a crucial problem, particularly with the explosive growth of RNA sequences in the postgenomic age. Unfortunately, so far no user-friendly web-server whatsoever has been developed to address such a problem. To meet the increasingly high demand from most experimental scientists working in the area of drug development, we have developed a new predictor called iRNAm5C-PseDNC by incorporating ten types of physical-chemical properties into pseudo dinucleotide composition via the auto/cross-covariance approach. Rigorous jackknife tests show that its anticipated accuracy is quite high. For most experimental scientists' convenience, a user-friendly web-server for the predictor has been provided at http://www.jci-bioinfo.cn/iRNAm5C-PseDNC along with a step-by-step user guide, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved. It has not escaped our notice that the approach presented here can also be used to deal with many other problems in genome analysis.
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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.