Graph-based artificial intelligence (ai) alignment of functional annotations from different ontologies using biological sequences

A computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to:

• identify associations of nodes between a first graph of a gene ontology capturing biological processes and a second graph of a protein function ontology;
• generate a composite graph by merging the first graph and the second graph using the associations of nodes;
• perform node embedding including node features of biological sequences for nodes of the composite graph;
• determine at least one new association of unassociated nodes of the composite graph using the node embeddings;
• and
• save the at least one new association of unassociated nodes of the composite graph as an association of nodes between the first graph and the second graph in persistent storage.

A system comprising:

• a processor, a computer readable memory, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to:
• identify associations of nodes between a first graph of a gene ontology capturing biological processes and a second graph of a protein function ontology;
• generate a composite graph by merging the first graph and the second graph using the associations of nodes;
• perform node embedding for nodes of the composite graph;
• build a graph neural network from the composite graph with the node embeddings;
• perform link prediction using the graph neural network to identify at least one new association of unassociated nodes of the composite graph;
• and
• save the at least one new association of unassociated nodes of the composite graph as an association of nodes between the first graph and the second graph in persistent storage.