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Background <p>The identification of lncRNA-miRNA interactions (LMIs) is crucial for deciphering post-transcriptional regulatory networks and their roles in development and disease. While computational methods have been developed to predict LMIs, existing approaches are often limited by an inability to effectively integrate multimodal biological data and to handle the severe class imbalance inherent to biological networks.</p> Results <p>To overcome these limitations, we present LMI-MHGAT, a novel deep learning framework for LMI prediction based on a Multilayer Heterogeneous Graph Attention network. Our model integrates diverse data—including RNA sequences, expression profiles, and known molecular interactions—into a unified graph representation. A key innovation is the use of a graph attention mechanism that dynamically learns to weight information from different relational layers, enabling the model to learn robust embeddings for lncRNAs and miRNAs. LMI-MHGAT significantly outperforms 14 existing methods on human LMI data, demonstrating exceptional robustness under severe class imbalance (positive-to-negative ratio 1:60). The model generalizes effectively, achieving state-of-the-art performance on rat and plant datasets. Case studies confirm its ability to recover disease-associated regulatory axes and predict novel, biologically plausible interactions.</p> Conclusions <p>LMI-MHGAT provides a more powerful and robust framework for LMI prediction by simultaneously addressing key limitations in data utilization and integration. The tool is freely accessible at <a href="https://github.com/Zhenpm/LMI-MHGAT">https://github.com/Zhenpm/LMI-MHGAT</a>.</p>

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Embedding multilayer RNA networks for lncRNA-miRNA interaction prediction via LMI-MHGAT

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月30日

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  • Jing Chen,
  • Peimeng Zhen,
  • Zhengxuan Liu,
  • Yongtian Wang,
  • Jiajie Peng,
  • Yifu Xiao,
  • Tao Wang

摘要

Background

The identification of lncRNA-miRNA interactions (LMIs) is crucial for deciphering post-transcriptional regulatory networks and their roles in development and disease. While computational methods have been developed to predict LMIs, existing approaches are often limited by an inability to effectively integrate multimodal biological data and to handle the severe class imbalance inherent to biological networks.

Results

To overcome these limitations, we present LMI-MHGAT, a novel deep learning framework for LMI prediction based on a Multilayer Heterogeneous Graph Attention network. Our model integrates diverse data—including RNA sequences, expression profiles, and known molecular interactions—into a unified graph representation. A key innovation is the use of a graph attention mechanism that dynamically learns to weight information from different relational layers, enabling the model to learn robust embeddings for lncRNAs and miRNAs. LMI-MHGAT significantly outperforms 14 existing methods on human LMI data, demonstrating exceptional robustness under severe class imbalance (positive-to-negative ratio 1:60). The model generalizes effectively, achieving state-of-the-art performance on rat and plant datasets. Case studies confirm its ability to recover disease-associated regulatory axes and predict novel, biologically plausible interactions.

Conclusions

LMI-MHGAT provides a more powerful and robust framework for LMI prediction by simultaneously addressing key limitations in data utilization and integration. The tool is freely accessible at https://github.com/Zhenpm/LMI-MHGAT.