Multi-Head Hypergraph Convolution With Feature Enhancement and Latent Representation Learning for miRNA–Disease Association Prediction

The association between miRNAs and diseases is crucial forunderstanding pathological mechanisms. However, existing methods often struggle to capture deep topological structures and suffer significant performance degradation under sparse associations. To address these challenges, we propose FKAMHV, a novel framework that integrates Fast Kolmogorov-Arnold Networks (FastKAN) and Multi-Head Hypergraph Convolution Networks (Multi-Head HGCN) to extract deep topological features, and incorporates $\beta$-Variational Autoencoder ($\beta$-VAE) to uncover latent associations. First, we construct heterogeneous networks based on functional, semantic, and Gaussian kernel similarities, and generate miRNA and disease specific hypergraphs using the K-nearest neighbors (KNN) algorithm. Next, FastKAN is employed to perform nonlinear modeling of node features, enhancing their representational capacity, and is combined with Multi-Head HGCN to strengthen the joint representation of structural and attribute information. To extract high-order topological features while avoiding redundancy, we introduce edge attention weights, a channel-wise squeeze-and-excitation (SE) mechanism, and a Jumping Knowledge strategy in the Multi-Head HGCN. Finally, $\beta$-VAE is used to model latent association distributions, where the $\beta$ coefficient serves as a regularization factor to balance reconstruction accuracy and latent space disentanglement, thereby improving generalization under sparse conditions. A learnable coefficient is introduced to fuse the outputs of the two modules for final prediction. Experimental results show that FKAMHV outperforms existing methods in terms of AUC and AUPR, and also achieves strong performance under sparse scenarios.