ArticleBioinformatics (Oxford, England)2022
m5c-iEnsem: 5-methylcytosine sites identification through ensemble models.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The trial behind it
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Who cites it
9 citing papers in PubMed.
- EvoRMD: integrating biological context and evolutionary RNA language models for interpretable prediction of RNA modifications.Genome biology · 2026Article
- Prediction ofFrontiers in medicine · 2026Article
- TCN-5mC: a predictor of 5-methylcytosine sites based on multi-feature fusion and TCN-inspired block networks.Frontiers in genetics · 2026Article
- Efficient blood cell classification from microscopic smear images using U-Net segmentation and a lightweight CNN.Scientific reports · 2025Article
- Artificial neural network-driven approaches to improved forecasting of disability care expenditures in an aging Kingdom of Saudi Arabia population.Scientific reports · 2025Article
- 2OM-Pred: prediction of 2-O-methylation sites in ribonucleic acid using diverse classifiers.Briefings in bioinformatics · 2025Article
- Advanced leukocyte classification using attention mechanisms and dual channel U-Net architecture.Scientific reports · 2025Article
- mRNA Transcript Variants Expressed in Mammalian Cells.International journal of molecular sciences · 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
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
motivation5-Methylcytosine (m5c), a modified cytosine base, arises from adding a methyl group at the 5th carbon position. This modification is a prevalent form of post-transcriptional modification (PTM) found in various types of RNA. Traditional laboratory techniques often fail to provide rapid and accurate identification of m5c sites. However, with the growing accessibility of sequence data, expanding computational models offers a more efficient and reliable approach to m5c site detection. This research focused on creating advanced in-silico methods using ensemble learning techniques. The encoded data was processed through ensemble models, including bagging and boosting techniques. These models were then rigorously evaluated through independent testing and 10-fold cross-validation.
resultsAmong the models tested, the Bagging ensemble-based predictor, m5C-iEnsem, demonstrated superior performance to existing m5c prediction tools. AVAILABILITY AND IMPLEMENTATION: To further support the research community, m5c-iEnsem has been made available via a user-friendly web server at https://m5c-iensem.streamlit.app/.
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.