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Background <p>N6-methyladenosine (m6A), the most predominant post-transcriptional RNA modification, regulates splicing, translation, and decay processes. Its dysregulation is implicated in cancers, metabolic disorders, and neurological diseases. Despite accumulating evidence highlighting m6A as a key player in human pathologies, no previous computational framework has investigated the high-order associations among m6A sites, diseases, and drugs within a unified model.</p> Results <p>Here, we introduce HNRM, a data-driven approach designed to model hyperedges across these entities. We frame this problem as a high-order link-prediction task on a hypergraph. We employ a hypergraph neural network based on hyperedge neighborhoods to learn embedding representations of both hyperedges and nodes.</p> Conclusions <p>The performance of HNRM is evaluated on a newly collected and processed m6A dataset, as well as on five additional datasets from other domains, demonstrating its superior effectiveness. Ablation studies and Gene Ontology enrichment analysis further validate its capability in identifying potential associations.</p>

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HNRM: hyperedge neighborhood-based representation for predicting N6-methyladenosine-related regulatory pathways

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月29日

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  • Dongdong Jiang,
  • Yan Li,
  • Liang Yu

摘要

Background

N6-methyladenosine (m6A), the most predominant post-transcriptional RNA modification, regulates splicing, translation, and decay processes. Its dysregulation is implicated in cancers, metabolic disorders, and neurological diseases. Despite accumulating evidence highlighting m6A as a key player in human pathologies, no previous computational framework has investigated the high-order associations among m6A sites, diseases, and drugs within a unified model.

Results

Here, we introduce HNRM, a data-driven approach designed to model hyperedges across these entities. We frame this problem as a high-order link-prediction task on a hypergraph. We employ a hypergraph neural network based on hyperedge neighborhoods to learn embedding representations of both hyperedges and nodes.

Conclusions

The performance of HNRM is evaluated on a newly collected and processed m6A dataset, as well as on five additional datasets from other domains, demonstrating its superior effectiveness. Ablation studies and Gene Ontology enrichment analysis further validate its capability in identifying potential associations.