A Dynamic Multi-Scale Hypergraph Learning Framework Driven by Features and Structures for ceRNA-Disease Association Prediction

作者：Xinfei Wang, Lan Huang, Yan Wang, Renchu Guan, Zhu‐Hong You, Fengfeng Zhou, Yuqing Li, Yuan Fu · 发表于：IEEE Journal of Biomedical and Health Informatics · 年份：2025 · DOI：10.1109/jbhi.2025.3602670 · 被引用次数：9 · 研究领域：Cancer-related molecular mechanisms research、MicroRNA in disease regulation、Molecular Biology Techniques and Applications

Competitive endogenous RNA (ceRNA) networks are pivotal for uncovering disease molecular mechanisms. Graph representation learning is a cornerstone for modeling biological regulatory networks and predicting disease-related biomarkers. However, current methods face challenges: traditional graph neural network (GNN) rely on low-order graph structures, which struggle to capture high-order molecular interactions, resulting in topological information loss; shallow GNN fail to model long-range dependencies, while deep architectures suffer from over-smoothing, limiting complex regulatory expression; static embeddings overlook dynamic molecular interactions, reducing biomarker accuracy. These limitations highlight the need for advanced graph learning frameworks. To address these challenges, we propose DMHLF, a Dynamic Multi-scale Hypergraph Learning Framework for predicting disease-associated ceRNA biomarkers. The framework first integrates multiple regulatory relationships among miRNAs, lncRNAs, circRNAs, mRNAs, and diseases to construct disease-specific ceRNA regulatory networks, capturing local and global regulatory patterns through multi-Hop hyperedges. Subsequently, we devise a Hypergraph-Weighted Dynamic Random Walk (HEDRW) method to dynamically extract node meta-embeddings that encode high-order regulatory information. Concurrently, we extend Eigen-GNN spectral analysis to hypergraph structures, incorporating a residual-enhanced hypergraph neural network to preserve the global...