Biomedical knowledge graph learning for drug repurposing by extending guilt-by-association to multiple layers.

ABSTRACT: Computational drug repurposing aims to identify new indications for existing drugs by utilizing high-throughput data, often in the form of biomedical knowledge graphs. However, learning on biomedical knowledge graphs can be challenging due to the dominance of genes and a small number of drug and disease entities, resulting in less effective representations. To overcome this challenge, we propose a "semantic multi-layer guilt-by-association" approach that leverages the principle of guilt-by-association - "similar genes share similar functions", at the drug-gene-disease level. Using this approach, our model DREAMwalk: Drug Repurposing through Exploring Associations using Multi-layer random walk uses our semantic information-guided random walk to generate drug and disease-populated node seque

SUBMITTER: Bang D 

PROVIDER: S-EPMC10272215 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature