ArticleComputational and structural biotechnology journal2025
m5CStack: An integrated framework for m5C site prediction using multi-feature stacking.
Article in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Prediction of DNA N4-Methylcytosine Sites Based on a Position-Aligned Multi-branch Fusion Network.Computational and structural biotechnology journal · 2026Article
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7 authors.
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Abstract
RNA 5-methylcytosine (m5C) modification sites are essential for understanding the regulation of RNA functions in various biological processes. However, the vast amount of sequence data generated by modern genomics poses significant challenges for traditional identification methods, which often struggle to meet high-throughput demands. Consequently, computational tools have become indispensable for predicting m5C sites. In this study, we present m5CStack, an advanced ensemble learning framework designed to predict m5C modification sites with high accuracy. m5CStack integrates multiple feature encoding techniques and machine learning models through a stacking architecture to enhance the robustness and reliability of predictions. We evaluate the framework on RNA datasets derived from multiple species, including
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