Learning multi-scale heterogenous network topologies and various pairwise attributes for drug-disease association prediction.
Journal:
Briefings in bioinformatics
Published Date:
Mar 10, 2022
Abstract
MOTIVATION: Identifying new therapeutic effects for the approved drugs is beneficial for effectively reducing the drug development cost and time. Most of the recent computational methods concentrate on exploiting multiple kinds of information about drugs and disease to predict the candidate associations between drugs and diseases. However, the drug and disease nodes have neighboring topologies with multiple scales, and the previous methods did not fully exploit and deeply integrate these topologies.