New drug discovery has always been a costly, time-consuming process with a high failure rate. Repurposing existing drugs offers a valuable alternative and reduces the risks associated with developing new drugs. Various experimental methods have been employed to facilitate drug repositioning
however, associations prediction between drugs and diseases through biological experiments is both expensive and time-consuming. Consequently, it is imperative to develop efficient and highly precise computational methods for predicting these associations. Based on this, we propose a drug-disease associations prediction method based on Hyperbolic Multivariate feature Learning in High-order Heterogeneous Networks for Drug-Disease Prediction, called H