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. A heterogeneous aspect comes from the graph having two or more types of nodes and two or more types of edges.
Usage
In recent studies on Alzheimer's drug repurposing (Reference 1) Graph Neural Networks (GNN) were used to identify biological interactions from prior knowledge databases containing information on effective treatments and genes associated with high risk. The nodes of the graph included drugs, genes, pathways and gene ontology (GO) 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. Note that in this paper the genes are encoded/embedded, simply meaning they are represented as numerical vectors/matrices to capture their function.
By keeping the focus previously approved drugs, the graph can identify synergy between medications that treat complex diseases.