Graph Attention Fusion With Kolmogorov-Arnold Network for Drug-Gene Interaction Prediction

Deep learning-based computational methods have emerged as powerful tools for predicting novel drug-gene interactions. It is essential to parse the joint influence of diverse attention focuses in large complex datasets in the model's decision-making process. Here, we propose graph attention fusion with Kolmogorov-Arnold network (KAN) for drug-gene interaction prediction (dgKAN). This approach parses the mutual influence of heterogeneous attention in drug-gene relationships by constructing an interpretable KAN network. Specifically, we use dynamic neighbor selection module by dynamic attention sampling to construct subgraphs and generate embedding representations for drugs and genes within these subgraphs. Then, we utilize a module consisted of Transformer and GNN architectures (TransGNN) to fuse the mechanism of global attention and local attention. Finally, we develop an interpretable KAN network with spline functions to model and analyze the cross-domain information flow between drugs and genes, enabling the prediction of drug-gene interactions. We conducted comprehensive experiments on various datasets, and the results demonstrate that dgKAN outperforms other baseline methods. Meanwhile, results illustrate that dgKAN captures the implicit characteristics by parsing heterogeneous attention in drug-gene relationships. The predicted drug-gene interactions have the potential to significantly aid in drug development for disease treatment.