GTFKAN: A Novel Microbe-drug Association Prediction Model Based on Graph Transformer and Fourier Kolmogorov-Arnold Networks

Abstract

Microbes have been shown to be closely related to human health. In recent years, lots of computational methods for predicting microbial-drug association have been proposed. In this manuscript, we introduced a novel predictive model, called GTFKAN, to identify potential microbe-drug associations by combining Graph Transformation Networks (GTN) with Fourier Kolmogorov-Arnold Networks (FKAN). In GTFKAN, we would first compute the Gaussian kernel and functional similarity of microbes and drugs respectively, and then adopt random walk and restart (RWR) methods to enhance these similar features to construct a new microbe-drug heterogeneous network HN. At the same time, we would further calculate the cosine similarity of microbes and diseases to construct another microbe-drug heterogeneous network LDIM. Next, we would input HN into GTN to derive the location and structural features of microorganisms and drugs, and input LDIM into FKAN to extract the hidden higher-order features of microorganisms and drugs, respectively. Finally, we would integrate these two features extracted by GTN and FKAN and feed the integrated features into the MLP classifier to infer potential microbial-drug associations. Moreover, to evaluate the performance of GTFKAN, we compared it with state-of-the-art methods based on well-known public datasets, and the experimental results show that GTFKAN can achieve satisfactory predictive performance. In addition, the results of ablation experiments and case studies also demonstrated the superiority of GTFKAN, which means that GTFKAN may be a useful microbial-drug association prediction tool in the future.