ArticleMolecular therapy : the journal of the American Society of Gene Therapy2023
XGBoost framework with feature selection for the prediction of RNA N5-methylcytosine sites.
Article in Molecular therapy : the journal of the American Society of Gene Therapy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed, 45 citations in OpenAlex.
- CMC-WDTK: CpG methylation change prediction by a weight-sharing dual-branch Transformer-Kolmogorov-Arnold network model.BMC genomics · 2026Article
- M6AREG 2.0: the landscape of m6A-centered crosstalk with diverse epigenetic regulation.Nucleic acids research · 2026Article
- Block sparse Bayes-based fuzzy system for RNA N6-methyladenosine sites prediction.PLoS computational biology · 2025Article
- PlantDeepMeth: A Deep Learning Model for Predicting DNA Methylation States in Plants.Plants (Basel, Switzerland) · 2025Article
- Review
- Recent Update on siRNA Therapeutics.International journal of molecular sciences · 2025Review
- An interpretable machine learning-assisted diagnostic model for Kawasaki disease in children.Scientific reports · 2025Article
- Exploring the methyl-verse: Dynamic interplay of epigenome and m6A epitranscriptome.Molecular therapy : the journal of the American Society of Gene Therapy · 2025Review
- XGBoost-enhanced ensemble model using discriminative hybrid features for the prediction of sumoylation sites.BioData mining · 2025Article
- Association between estimated glucose disposal rate and cardiovascular diseases in patients with diabetes or prediabetes: a cross-sectional study.Cardiovascular diabetology · 2025Article
- m5CStack: An integrated framework for m5C site prediction using multi-feature stacking.Computational and structural biotechnology journal · 2025Article
- Rore: robust and efficient antioxidant protein classification via a novel dimensionality reduction strategy based on learning of fewer features.Genomics & informatics · 2024Article
- RNA-ModX: a multilabel prediction and interpretation framework for RNA modifications.Briefings in bioinformatics · 2024Article
- MultiFeatVotPIP: a voting-based ensemble learning framework for predicting proinflammatory peptides.Briefings in bioinformatics · 2024Article
- Platelet Metabolites as Candidate Biomarkers in Sepsis Diagnosis and Management Using the Proposed Explainable Artificial Intelligence Approach.Journal of clinical medicine · 2024Article
- Identification of microbe-disease signed associations via multi-scale variational graph autoencoder based on signed message propagation.BMC biology · 2024Article
- ac4C-AFL: A high-precision identification of human mRNA N4-acetylcytidine sites based on adaptive feature representation learning.Molecular therapy. Nucleic acids · 2024Article
- A CNN based m5c RNA methylation predictor.Scientific reports · 2023Article
- Cancer Diagnosis through Contour Visualization of Gene Expression Leveraging Deep Learning Techniques.Diagnostics (Basel, Switzerland) · 2023Article
- Enhancing Taxonomic Categorization of DNA Sequences with Deep Learning: A Multi-Label Approach.Bioengineering (Basel, Switzerland) · 2023Article
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 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
5-methylcytosine (m5C) is indeed a critical post-transcriptional alteration that is widely present in various kinds of RNAs and is crucial to the fundamental biological processes. By correctly identifying the m5C-methylation sites on RNA, clinicians can more clearly comprehend the precise function of these m5C-sites in different biological processes. Due to their effectiveness and affordability, computational methods have received greater attention over the last few years for the identification of methylation sites in various species. To precisely identify RNA m5C locations in five different species including Homo sapiens, Arabidopsis thaliana, Mus musculus, Drosophila melanogaster, and Danio rerio, we proposed a more effective and accurate model named m5C-pred. To create m5C-pred, five distinct feature encoding techniques were combined to extract features from the RNA sequence, and then we used SHapley Additive exPlanations to choose the best features among them, followed by XGBoost as a classifier. We applied the novel optimization method called Optuna to quickly and efficiently determine the best hyperparameters. Finally, the proposed model was evaluated using independent test datasets, and we compared the results with the previous methods. Our approach, m5C- pred, is anticipated to be useful for accurately identifying m5C sites, outperforming the currently available state-of-the-art techniques.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.