Sinha, M.; Tu, R.; Gonzalez, C.; Su, A. I.

This study introduces a weighted ensemble method called \"WeightedKgBlend\'\' for link prediction in knowledge graphs which combines the predictive capabilities of two types of Knowledge Graph completion methods: knowledge graph embedding and path based reasoning. By dynamically assigning weights based on individual model performance, WeightedKgBlend surpasses standalone methods, demonstrating superior predictive accuracy when tested to discover drug-disease candidates over a large-scale biomedical knowledge graph called Mechanistic Repositioning Network. This research highlights the efficacy of an integrated approach combining multiple methods in drug discovery, showcasing improved performance and the potential for transformative insights in the realm of biomedical knowledge discovery.