Hypergraph-Enhanced Multimodal Dynamics for Multi-Omics Classification via Intra- and Inter-Sample Fusion

Multimodal learning is increasingly pivotal in biomedical research, where multi-omics technologies enable comprehensive characterization of diverse molecular layers. Despite their potential, effective integration of heterogeneous multi-omics data remains challenging due to high dimensionality, modality inconsistency, and complex inter-sample dependencies. Existing methods primarily focus on intra-sample feature fusion, often overlooking high-order structural relationships across samples, which limits their ability to capture system-level interactions. To address these challenges, we propose HyperGraph-Enhanced MultiModal Dynamics (HGEMMD), a novel framework that simultaneously models intra-sample modality fusion and inter-sample high-order associations for robust clinical multi-omics integration and classification. HGEMMD incorporates a multimodal dynamics module to alleviate data sparsity and modality heterogeneity, and constructs a modality-aware hypergraph where each hyperedge connects semantically or functionally related samples, capturing non-pairwise dependencies. To further enhance robustness and structural coherence, we introduce relational consistency learning that aligns sample-level relational patterns before and after hypergraph propagation, preserving local semantics while ensuring global structural information. Extensive experiments on benchmark multi-omics datasets demonstrate that HGEMMD consistently outperforms state-of-the-art approaches, validating its effectiveness in robust and trustworthy clinical multi-omics integration. Our code and data are available at https://github.com/ljw-struggle/HGEMMD.