Fourier Transform and Kolmogorov-Arnold Network Enhanced Relation-Aware Generative and Adversarial Network for miRNA-Disease Association Prediction

Identifying the miRNAs that are associated with diseases can assist to explore the pathogenesis of diseases. Traditional prediction methods primarily focus on integrating multi-sourced data related to miRNAs and diseases within euclidean space to infer potential candidate disease-related miRNAs. Research indicates that miRNAs belonging to the same family, residing in the same cluster, or sharing more common target proteins are more likely to be involved into similar disease processes. However, existing approaches have not fully integrated the family, cluster, and common target protein attributes of miRNAs, nor explored the low-frequency smoothness and high-frequency local fluctuation features of miRNA and disease nodes in the frequency domain. To overcome these issues, we propose a Fourier transform and Kolmogorov-Arnold network enhanced relation-aware generative adversarial network (FKRGAN) model. FKRGAN incorporates a Fourier transform enhanced dual-space feature learning (FDFL) strategy, which helps learn the topological features of miRNA and disease nodes in euclidean space, as well as their low-frequency and high-frequency characteristics in the frequency domain. Furthermore, a feature-level attention mechanism is designed to determine the significance of features learned from dual-space representations and the family, cluster, and target protein features derived from homogeneous graphs, thereby facilitating the adaptive fusion of these features. We develop a relation-aware generative adversarial network with Kolmogorov-Arnold networks (KRGAN), which enhances feature learning for each miRNA and disease node by employing generative and adversarial strategies. The generator, composed of multi-layer Kolmogorov-Arnold networks (KAN), fuses multiple connection relationships between miRNA and disease nodes during the generation process, effectively capturing nonlinear dependencies among node features to produce relation-aware node feature representations. Comparative experiments on public datasets demonstrate that our method outperforms eight state-of-the-art prediction methods. Case studies on three diseases further show FKRGAN's ability to identify candidate miRNA-disease associations.