The effects of biological knowledge graph topology on embedding-based link prediction

Abstract

AbstractDue to the limited information available about rare diseases and their causal variants, knowledge graphs are often used to augment our understanding and make inferences about new gene-disease connections. Knowledge graph embedding methods have been successfully applied to various biomedical link prediction tasks but have yet to be adopted for rare disease variant prioritization. Here, we explore the effect of knowledge graph topology on Knowledge graph embedding link prediction performance and challenge the assumption that massively aggregating knowledge graphs is beneficial in deciphering rare disease cases and improving outcomes. We find that using a filtered version of the Monarch knowledge graph with only 11% of the size of the full knowledge graph results in improved model predictive performance. Additionally, we found that as information is aggregated and re-added to the knowledge graph, performance improvements are driven by the quality of information, not the quantity.