HpMiX: A Disease ceRNA biomarker prediction framework driven by graph topology-constrained Mixup and hypergraph residual enhancement

The competing endogenous RNA (ceRNA) regulatory network (CENA) plays a critical role in elucidating the molecular mechanisms of diseases. However, existing computational methods primarily focus on modeling local topological structures of biological networks, struggling to capture high-order regulatory relationships and global topological structures, thus limiting a deeper understanding of complex regulatory interactions. To address this, we propose HpMiX, a Graph Topology-Constrained Mixup (GTCM) and hypergraph residual enhancement learning framework for the discovery of disease-related ceRNA biomarkers. This framework first constructs a CENA network encompassing multi-molecule associations, including miRNA, lncRNA, circRNA, and mRNA, and models higher-order regulatory relationships using K-hop hyperedges. Biologically meaningful initial features are then extracted from CENA via a multi-structure hypergraph weighted random walk method (MHWRW), integrating prior biological knowledge and regulatory information. Subsequently, graph topology-constrained Mixup and multi-head attention, combined with a residual hypergraph neural network, are employed to generate robust node embeddings with both local and global context, enabling the identification of potential disease-ceRNA biomarkers. Prediction results across multiple disease biomarkers demonstrate that HpMiX significantly outperforms state-of-the-art methods, validating its effectiveness in biological regulatory network representation learning. Case studies further confirm that the framework can effectively identify differentially expressed ceRNAs in diseases, highlighting its potential as a tool for pre-screening high-probability disease biomarkers.