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.

Diffusion models (DMs) have demonstrated remarkable ability to generate diverse and high-quality images by efficiently modeling complex data distributions. They have also been explored as powerful generative priors for signal recovery, resulting in a substantial improvement in the quality of reconstructed signals. However, existing research on signal recovery with diffusion models either focuses on specific reconstruction problems or is unable to handle nonlinear measurement models with discontinuous or unknown link functions. In this work, we focus on using DMs to achieve accurate recovery from semi-parametric single index models, which encompass a variety of popular nonlinear models that may have {\em discontinuous} and {\em unknown} link functions. We propose an efficient reconstruction method that only requires one round of unconditional sampling and (partial) inversion of DMs. Theoretical analysis on the effectiveness of the proposed methods has been established under appropriate conditions. We perform numerical experiments on image datasets for different nonlinear measurement models. We observe that compared to competing methods, our approach can yield more accurate reconstructions while utilizing significantly fewer neural function evaluations.