Framework reveals f5C modification site predictions in RNA, suggesting a novel approach for RNA modifications study.
Notably, our model's learned sequence patterns strongly agree with known RNA regulatory motifs identified via MEME, indicating biological interpretability. The proposed framework not only offers a robust computational tool for f5C research but also establishes a transferable architecture for studying other RNA modifications. The source code and datasets are publicly available at: https://github.com/HuiCong123/Fusion_f5C-Pred .
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Hui et al. (2025) studied this question.
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