A bijective inference network for interpretable identification of RNA N6-methyladenosine modification sites
Abstract: Highlights•Incorporate RNA secondary structure to enhance accuracy and interpretability of m6A site prediction;•Propose a bijective attribution paradigm to link model outputs with biological insights.•Validate m6A-BIN’s accuracy and interpretability on 11 datasets for m6A site identification.
External IDs:dblp:journals/pr/LiYLSZHH25
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