Drug Repositioning using Consilience of Knowledge Graph Completion Methods

Abstract

AbstractMotivationWhile link prediction methods in knowledge graphs have been increasingly utilized to locate potential associations between compounds and diseases, they suffer from lack of sufficient evidence to explain why a drug and a disease may be indicated. This is especially true for knowledge graph embedding (KGE) based methods where a drug-disease indication is linked only by information gleaned from a vector representation. Complementary pathwalking algorithms can increase the confidence of drug repositioning candidates by traversing a knowledge graph. However, these methods heavily weigh the relatedness of drugs, through their targets, pharmacology or shared diseases. Furthermore, these methods rely on arbitrarily extracted paths as evidence of a compound to disease indication and lack the ability to make predictions on rare diseases.ResultsIn this paper, we evaluate seven link prediction methods on a vast biomedical knowledge graph for drug repositioning. We follow the principle of consilience, and combine the reasoning paths and predictions provided by path-based and KGE methods to not only demonstrate a significant ranking performance improvement but also identify putative drug repositioning indications. Finally, we highlight the utility of our approach through a potential repositioning indication.AvailabilityThe MIND dataset can be found at 10.5281/zenodo.8117748. The python code to reproduce the entirety of this analysis can be found athttps://github.com/SuLab/{KnowledgeGraphEmbedding, CBRonMRN}.ContactAndrew I. Su atasu@scripps.eduSupplementary informationSupplementary data are available atThe Journal Titleonline.