NamE: Capturing Biological Context in KGEs via Contextual Named Graph Embeddings

Large biological databases have become standard in many fields of biology, particularly biomedicine. Much of this data (such as that from DrugBank, PharmKG, PrimeKG, BIOSNAP, and others) is now expressed in a Knowledge Graph (KG) format in which concepts (such as drugs and diseases) are represented as nodes and the relationships between then (such as clinical drug indications) are represented by edges. Bioinformatics pipelines that leverage this data commonly make use of Knowledge Graph Embeddings (KGEs) to learn and analyse the data at hand. While existing work demonstrates that this can effectively assist in a variety of biomedical tasks, existing KGE approaches remain limited by their inability to account for relevant biological context.In this paper, we present a novel KGE framework, called NamE, that solves this problem by explicitly modelling for context in KGE systems. In experiments on two applied bioinformatics use-cases, predicting drug-drug interactions and predicting clinical indications of drugs, we show that NamE substantially improves the ability of KGEs to make accurate predictions by up to 72.2% and 90.9% respectively as compared to conventional KGE methods.