ArticleMolecular therapy. Nucleic acids2020
Prediction of m5C Modifications in RNA Sequences by Combining Multiple Sequence Features.
Article in Molecular therapy. Nucleic acids, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
31 citing papers in PubMed, 51 citations in OpenAlex.
- ac4C modification sites prediction in human mRNA: a complete review.Briefings in bioinformatics · 2026Review
- TET3-Mediated m5C Modification of CCAT2 Accelerates Cardiac Microvascular Endothelial Cell Damage in Acute Coronary Syndrome.The Kaohsiung journal of medical sciences · 2026Article
- From nucleotides to numbers: a comprehensive review of RNA feature extraction methods for computational modelling.Briefings in bioinformatics · 2025Review
- NSUN2 promotes leukemogenesis by ferritin heavy chain 1-mediated anti-ferroptosis and enhancing global protein synthesis in an mClinical epigenetics · 2025Article
- Article
- FusDRM-m5C: a hybrid model for accurate prediction of 5-methylcytosine modification sites based on feature fusion and attention mechanism.Frontiers in genetics · 2025Article
- Recent research advances in RNA m5C methylation modification in liver diseases.Frontiers in molecular biosciences · 2025Review
- Human essential gene identification based on feature fusion and feature screening.IET systems biology · 2024Article
- A predictive approach for host-pathogen interactions using deep learning and protein sequences.Virusdisease · 2024Article
- A CNN based m5c RNA methylation predictor.Scientific reports · 2023Article
- m6Aminer: Predicting the m6Am Sites on mRNA by Fusing Multiple Sequence-Derived Features into a CatBoost-Based Classifier.International journal of molecular sciences · 2023Article
- Self-attention enabled deep learning of dihydrouridine (D) modification on mRNAs unveiled a distinct sequence signature from tRNAs.Molecular therapy. Nucleic acids · 2023Article
- Contribution of m5C RNA Modification-Related Genes to Prognosis and Immunotherapy Prediction in Patients with Ovarian Cancer.Mediators of inflammation · 2023Article
- EMDL-ac4C: identifying N4-acetylcytidine based on ensemble two-branch residual connection DenseNet and attention.Frontiers in genetics · 2023Article
- 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
- Circ-LocNet: A Computational Framework for Circular RNA Sub-Cellular Localization Prediction.International journal of molecular sciences · 2022Article
- Article
- Immune Infiltrates of m5C RNA Methylation-Related LncRNAs in Uterine Corpus Endometrial Carcinoma.Journal of oncology · 2022Article
- Dynamic regulation and key roles of ribonucleic acid methylation.Frontiers in cellular neuroscience · 2022Review
- Biological Sequence Classification: A Review on Data and General Methods.Research (Washington, D.C.) · 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
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
5-Methylcytosine (m5C) is a well-known post-transcriptional modification that plays significant roles in biological processes, such as RNA metabolism, tRNA recognition, and stress responses. Traditional high-throughput techniques on identification of m5C sites are usually time consuming and expensive. In addition, the number of RNA sequences shows explosive growth in the post-genomic era. Thus, machine-learning-based methods are urgently requested to quickly predict RNA m5C modifications with high accuracy. Here, we propose a noval support-vector-machine (SVM)-based tool, called iRNA-m5C_SVM, by combining multiple sequence features to identify m5C sites in Arabidopsis thaliana. Eight kinds of popular feature-extraction methods were first investigated systematically. Then, four well-performing features were incorporated to construct a comprehensive model, including position-specific propensity (PSP) (PSNP, PSDP, and PSTP, associated with frequencies of nucleotides, dinucleotides, and trinucleotides, respectively), nucleotide composition (nucleic acid, di-nucleotide, and tri-nucleotide compositions; NAC, DNC, and TNC, respectively), electron-ion interaction pseudopotentials of trinucleotide (PseEIIPs), and general parallel correlation pseudo-dinucleotide composition (PC-PseDNC-general). Evaluated accuracies over 10-fold cross-validation and independent tests achieved 73.06% and 80.15%, respectively, which showed the best predictive performances in A. thaliana among existing models. It is believed that the proposed model in this work can be a promising alternative for further research on m5C modification sites in plant.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.