Original paper
HINGRL: predicting drug–disease associations with graph representation learning on heterogeneous information networks
Abstract
Identifying new indications for drugs plays an essential role at many phases of drug research and development. Computational methods are regarded as an effective way to associate drugs with new indications. However, most of them complete their tasks by constructing a variety of heterogeneous networks without considering the biological knowledge of drugs and diseases, which are believed to be useful for improving the accuracy of drug...
Paper Details
Title
HINGRL: predicting drug–disease associations with graph representation learning on heterogeneous information networks
Published Date
Nov 16, 2021
Journal
Volume
23
Issue
1