Hyperbolic multivariate feature learning in higher-order heterogeneous networks for drug-disease prediction.
Journal:
Artificial intelligence in medicine
PMID:
39985835
Abstract
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 HML. Our approach begins by mining high-order information from protein-disease and drug-protein networks to construct high-order heterogeneous networks. Subsequently, we employ multivariate feature learning to create hyperbolic representations, and then enhance the features of the heterogeneous network. Finally, we utilize a hyperbolic graph attention network in the hyperbolic space to aggregate neighbor information and perform the final prediction task. In addition, we evaluate the performance of HML by comparing it with some state-of-the-art methods across different datasets. The case study further validate the effectiveness of HML. Our implementation will be publicly available at: https://github.com/jianruichen/H-3ML.