Heterogeneous Graph Learning
Knowledge graphs are visualization of information with multi-type relations (edges) among some multi-type entities (nodes) within an environment of interest.
In recent studies on Alzheimer's drug development (1) Graph Neural Networks (GNN) were used to identify biological interactions from prior knowledge. The nodes of the graph included drugs, genes, pathways and gene ontology, connected by interactions including drug-target interaction, drug-drug structural similarity, gene-gene interaction, gene-pathway association, gene-GO association and drug-GO association. A comprehensive graph can be created combining existing information from multiple sources on the biological interactions of complex drug-gene relationships, and from there we can use machine learning to train a model and address the incomplete knowledge in the graph.