Biomedical Knowledge Graph Refinement and Completion using Graph Representation Learning and Top-K Similarity Measure

TL;DR

This paper proposes a method using graph representation learning and top-K similarity measures to refine and complete biomedical knowledge graphs, aiming to improve drug discovery accuracy by predicting missing links.

Contribution

It introduces a novel approach combining graph embeddings and top-K similarity for biomedical knowledge graph completion and refinement.

Findings

Effective link prediction using top-K cosine similarity

Comparable performance to binary classifiers in link prediction

Demonstrated on the Chem2Bio2RD biomedical knowledge graph

Abstract

Knowledge Graphs have been one of the fundamental methods for integrating heterogeneous data sources. Integrating heterogeneous data sources is crucial, especially in the biomedical domain, where central data-driven tasks such as drug discovery rely on incorporating information from different biomedical databases. These databases contain various biological entities and relations such as proteins (PDB), genes (Gene Ontology), drugs (DrugBank), diseases (DDB), and protein-protein interactions (BioGRID). The process of semantically integrating heterogeneous biomedical databases is often riddled with imperfections. The quality of data-driven drug discovery relies on the accuracy of the mining methods used and the data's quality as well. Thus, having complete and refined biomedical knowledge graphs is central to achieving more accurate drug discovery outcomes. Here we propose using the latest graph representation learning and embedding models to refine and complete biomedical knowledge graphs. This preliminary work demonstrates learning discrete representations of the integrated biomedical knowledge graph Chem2Bio2RD [3]. We perform a knowledge graph completion and refinement task using a simple top-K cosine similarity measure between the learned embedding vectors to predict missing links between drugs and targets present in the data. We show that this simple procedure can be used alternatively to binary classifiers in link prediction.