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Background <p>Long non-coding RNAs (lncRNAs) regulate diverse biological processes via protein interactions, and disruptions in these interactions are frequently associated with complex diseases. Network-based computational methods show promise in predicting such interactions, yet most cannot generalize to new, unseen molecules.</p> Results <p>Here, we introduce PRInter, an inductive multimodal framework integrating sequence features with heterogeneous network structures for lncRNA-protein interaction prediction. PRInter employs meta-path-guided random walks to capture network context and utilizes contrastive learning to refine molecular sequence embeddings, facilitating the prediction of candidate lncRNA-protein pairs involving molecules beyond the training network. To improve the reliability of evaluation, we adopt a degree-balanced negative sampling strategy to reduce potential bias from scale-free network topology. Comprehensive experiments on three public datasets show that PRInter achieves an AUC of 0.882 and performs favorably compared with representative baseline methods.</p> Conclusions <p>As an inductive and less biased model, PRInter offers a robust basis for large-scale exploration of lncRNA-protein interactions and supports future applications in functional genomics and drug discovery.</p>

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PRInter: an inductive multimodal heterogeneous network embedding framework with contrastive learning for lncRNA-protein interaction prediction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月30日

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  • Guoqing Zhao,
  • Baolu Shi,
  • Pengpai Li,
  • Zhi-Ping Liu

摘要

Background

Long non-coding RNAs (lncRNAs) regulate diverse biological processes via protein interactions, and disruptions in these interactions are frequently associated with complex diseases. Network-based computational methods show promise in predicting such interactions, yet most cannot generalize to new, unseen molecules.

Results

Here, we introduce PRInter, an inductive multimodal framework integrating sequence features with heterogeneous network structures for lncRNA-protein interaction prediction. PRInter employs meta-path-guided random walks to capture network context and utilizes contrastive learning to refine molecular sequence embeddings, facilitating the prediction of candidate lncRNA-protein pairs involving molecules beyond the training network. To improve the reliability of evaluation, we adopt a degree-balanced negative sampling strategy to reduce potential bias from scale-free network topology. Comprehensive experiments on three public datasets show that PRInter achieves an AUC of 0.882 and performs favorably compared with representative baseline methods.

Conclusions

As an inductive and less biased model, PRInter offers a robust basis for large-scale exploration of lncRNA-protein interactions and supports future applications in functional genomics and drug discovery.