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By connecting diverse entities and relationships, biomedical knowledge graphs hold the potential to uncover new insights from existing data. This webinar introduces a machine learning-based approach that utilizes causal interactions from the QIAGEN Knowledge Graph to predict novel drug-disease relationships and construct networks that capture relevant supporting evidence. We will present examples that showcase the application of this approach in the context of drug repurposing.
