ArticleFrontiers in genetics2022
m5Cpred-XS: A New Method for Predicting RNA m5C Sites Based on XGBoost and SHAP.
Article in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Hepatitis B virus promotes hepatocellular carcinoma progression by disrupting NSUN2/YBX1-mediated mVirologica Sinica · 2026Article
- An efficient method for network traffic anomaly detection based on SHAP and deep learning.PloS one · 2026Article
- TCN-5mC: a predictor of 5-methylcytosine sites based on multi-feature fusion and TCN-inspired block networks.Frontiers in genetics · 2026Article
- Epigenomic Transcriptome Regulation of Growth and Development and Stress Response in Cucurbitaceae Plants: The Role of RNA Methylation.Current issues in molecular biology · 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
- i5mC-DCGA: an improved hybrid network framework based on the CBAM attention mechanism for identifying promoter 5mC sites.BMC genomics · 2024Article
- 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
- 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
- m5c-iEnsem: 5-methylcytosine sites identification through ensemble models.Bioinformatics (Oxford, England) · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As one of the most important post-transcriptional modifications of RNA, 5-cytosine-methylation (m5C) is reported to closely relate to many chemical reactions and biological functions in cells. Recently, several computational methods have been proposed for identifying m5C sites. However, the accuracy and efficiency are still not satisfactory. In this study, we proposed a new method, m5Cpred-XS, for predicting m5C sites of
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Registered trials
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