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.
EC-KG is an interoperable, provenance-first KG which integrates RTX-KG2, ROBOKOP, and PrimeKG at the network-level, encapsulating over 7 million nodes and 81 million edges from 95 primary data sources, and has improved coverage of core biomedical entities, and captures complex biomedical mechanisms within its topology.
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...
Link prediction validated PregBase's inference capability beyond extracted knowledge, recovering established clinical interventions, reconstructing canonical hormonal pathways across maternal-placental-fetal compartments, and identifying novel biomarker candidates for preterm birth, gestational diabetes, and preeclamps...
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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