iDRKAN: Interpretable miRNA-Disease Association Prediction Based on Dual-Graph Representation Learning and Kolmogorov-Arnold Network

Abstract

Accurately identifying miRNA-disease association (MDA) is of great importance in biomedical research and clinical applications. However, most existing computational methods rely on similarity, making it difficult to effectively capture the deep semantic information among heterogeneous nodes in complex networks. In addition, the inherent "black-box" nature of traditional deep learning models leads to a lack of transparency in their decision-making process. Therefore, we propose an interpretable MDA prediction method (iDRKAN) based on dual-graph representation learning and Kolmogorov-Arnold Network. First, iDRKAN constructs similarity views and meta-path views based on the similarity matrix and association matrix respectively, and learns higher-order feature representation of each view by graph convolutional network (GCN). Subsequently, the multi-channel attention (MCA) mechanism is introduced to adaptively fuse the contextual information of each similarity view in different convolutional layers, and the semantic layer attention (SLA) mechanism is used to integrate the semantic information contained in different meta-path views. Next, the contrastive learning strategy is used to optimize the consistency between the dual-graph representation. Finally, the dual-graph features are weighted fused, and fed into the interpretable Kolmogorov-Arnold Network (KAN) for prediction. Experimental results on two public datasets show that iDRKAN significantly outperforms existing computational approaches in multiple performance indicators. In addition, experiments with different classifiers verify that iDRKAN achieves a good balance between prediction performance and interpretability. The case study further demonstrates the effectiveness of iDRKAN in mining potential associations.