Prediction of drug-disease associations based on reinforcement symmetric metric learning and graph convolution network.

ABSTRACT: Accurately identifying novel indications for drugs is crucial in drug research and discovery. Traditional drug discovery is costly and time-consuming. Computational drug repositioning can provide an effective strategy for discovering potential drug-disease associations. However, the known experimentally verified drug-disease associations is relatively sparse, which may affect the prediction performance of the computational drug repositioning methods. Moreover, while the existing drug-disease prediction method based on metric learning algorithm has achieved better performance, it simply learns features of drugs and diseases only from the drug-centered perspective, and cannot comprehensively model the latent features of drugs and diseases. In this study, we propose a novel drug repositioning

SUBMITTER: Luo H 

PROVIDER: S-EPMC10879308 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature