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<p>RNA modifications regulate post transcriptional gene expression, yet most computational methods model each modification independently and overlook competition among modification types at a single site. We present EvoRMD, a biologically contextualized and interpretable framework for RNA modification prediction. EvoRMD combines RNA language model embeddings with structured metadata, including species, organ, cell type, and subcellular localization, and uses attention to identify informative sequence positions. A shared multiclass classifier produces context conditioned predictions across 11 modification types. EvoRMD achieves strong performance and provides interpretable insights through attention patterns and motif analyses, supporting biologically grounded prioritization of candidate RNA modifications.</p>

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EvoRMD: integrating biological context and evolutionary RNA language models for interpretable prediction of RNA modifications

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月2日

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  • Bo Wang,
  • Hao Zhang,
  • Taoyong Cui,
  • Xiaoyu Wang,
  • Jiangning Song,
  • Hao Xu

摘要

RNA modifications regulate post transcriptional gene expression, yet most computational methods model each modification independently and overlook competition among modification types at a single site. We present EvoRMD, a biologically contextualized and interpretable framework for RNA modification prediction. EvoRMD combines RNA language model embeddings with structured metadata, including species, organ, cell type, and subcellular localization, and uses attention to identify informative sequence positions. A shared multiclass classifier produces context conditioned predictions across 11 modification types. EvoRMD achieves strong performance and provides interpretable insights through attention patterns and motif analyses, supporting biologically grounded prioritization of candidate RNA modifications.