TL;DR
By unifying fragmented research and enabling robust data exploration, UKB-KG emerges as a powerful tool for advancing biomedical research and driving innovative healthcare applications.
KRAKEN (Knowledge Research & Analysis Kit for Evidence Networks) addresses this gap by integrating existing graphs with specialized sources such as RefMet, LIPID MAPS, NIH Common Data Elements, Polygenic Score Catalog, and derived wellness measures including biological age and biological BMI.
Identifying causal connections between existing drugs and mechanistic profiles of diseases is a foundational step for effective drug repurposing. Although knowledge graphs (KGs) are highly suited for consolidating biomedical databases and tracking these connections, a single biomedical KG is constrained by its ingestio...
KGDDA is proposed, a multimodal framework designed for drug-disease association prediction that synergistically integrates KGs with medical ontologies, enabling the adaptive capture of intricate drug-disease interactions.
A knowledge graph generation pipeline that extracts subject-predicate-object triplets representing scientific claims from research articles and implements a multi-stage consolidation process focused on normalizing entities and relations, offering a robust framework for reducing ambiguity and improving the interoperabil...
Pregnancy complications are a leading cause of maternal and neonatal mortality worldwide. Understanding their underlying mechanisms is hindered by dispersed evidence across thousands of studies and complex biological interactions. We present PregBase, a comprehensive knowledge base for pregnancy research comprising aut...
Initial experiments on heart-failure-focused clinical question answering show that CGX improves evidence retrieval quality and perceived answer reliability over conventional retrieval methods, while reducing total graph construction time by 69.7% under the same input corpus and hardware setting.
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