KRAKEN: A provenance-tracked knowledge graph for multiomic and wellness research

Every Cure Knowledge Graph: A Unified Biomedical Knowledge Graph for Drug Repurposing

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 ingestion pipeline and knowledge sources. While different biomedical KGs could be complementary if combined, efforts to combine them into a unified and more comprehensive KG are hindered by lack of interoperability and poor provenance. To address those issues, we present EC-KG, a Biolink Model-compatible KG for computational drug repurposing. 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. EC-KG has improved coverage of core biomedical entities such as drugs, targets, and diseases relevant to drug repurposing vs source graphs, and captures complex biomedical mechanisms within its topology. We demonstrate that the network unification in EC-KG leads to emergence of novel, mechanistically relevant pathways which are disconnected in the underlying constituent networks and show its applications in method development, benchmarking and predictive drug repurposing applications. EC-KG has already been successfully used in drug repurposing research to surface Botulinum Toxin A as a candidate to treat Major Depressive Disorder, as well as to validate repurposing of Lenalidomide and Dexamethasone for a subgroup of patients with Rosai-Dorfman Disease.

UKB-KG: Knowledge Graph for Integrating and Enhancing Biomedical Insights from the UK Biobank

CGX: OCR-enhanced knowledge graph retrieval for explainable heart failure analysis

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.

PrimeKG-Plus: a refreshed and rare-disease-enriched precision medicine knowledge graph

Application of knowledge graphs in health management for chronic disease patients: a scoping review

Chronic diseases impose a significant burden on global health. Knowledge graphs (KGs), which integrate and represent multisource information as interconnected networks, provide a promising approach for enabling personalized and dynamic health management. The aim of this scoping review is to systematically map the current landscape of KG applications in chronic disease health management. In accordance with the Arksey and O’Malley framework and the PRISMA-ScR guidelines, a systematic search was conducted in eight databases, namely, the Wanfang Database, CNKI, VIP, SinoMed, PubMed, Embase, Web of Science, and CINAHL, from the establishment of the databases to August 2025. Two researchers independently screened the literature and extracted data on the basis of the inclusion and exclusion criteria. A total of 15 studies published between 2018 and 2025 were included. In these studies, the KG construction process generally involved five stages: data collection, information extraction, knowledge fusion, graph construction, and visualization. KGs were applied across a range of chronic diseases, including metabolic, cardiovascular, and respiratory diseases, as well as cancers. In terms of application scenarios, self-health management was the most common (9 studies, 60.0%), followed by clinical decision support (5 studies, 33.3%), intelligent question answering (4 studies, 26.7%), and health monitoring/early warning (1 study, 6.7%). With respect to system maturity, 12 studies (80.0%) were classified as prototype systems, 3 studies (20.0%) were classified as pilot implementations, and none were classified as clinically deployed systems. The reported outcomes were mainly technical or feasibility oriented, and 7 studies (46.7%) did not report any explicit evaluation metrics. Knowledge graphs show promise for integrating heterogeneous health data in chronic disease management. However, research has focused predominantly on system development and proof-of-concept validation, with limited high-quality evidence regarding clinical effectiveness. To support clinically meaningful implementation, in future work, rigorous real-world evaluation should be prioritized and challenges related to data quality, interoperability, and model interpretability should be addressed.

MolBioKG: Grounding Out-of-Graph Molecules in Biomedical Knowledge Graphs via Multi-Resolution Structural Anchoring

This work introduces MolBioKG, a two-layer system that grounds unseen molecules in biomedical evidence via multi-resolution structural anchoring and outperforms strong baselines across in-graph link recovery, complex multi-hop reasoning, and out-of-graph generalization.