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.
Piotr Kaniewski, E. K. Carter, Daniel J. Rhodes et al.· bioRxiv· 0 citations
Summary Background Vaccination is a vital tool in preventing acute COVID-19 and may confer additional protection against Long COVID, although it is unclear whether this protection wanes over time. Methods We assessed electronic health record (EHR) data from a national, retrospective cohort of patients, comparing the 12-month cumulative incidence of Long COVID (ICD-10 code U09.9) among (A) patients who were vaccinated versus unvaccinated (two or more versus zero doses) and (B) patients diagnosed with acute COVID-19 1–3 months, 3–5 months, or 5–7 months after vaccination. Findings In our binary cohort (n = 519,980), we found that patients who were vaccinated had a lower risk of Long COVID (adjusted risk ratio 0.84 (0.81, 0.88)) or mortality (adjusted risk ratio 0.83 (0.81, 0.86)) than patients who were unvaccinated. In our longitudinal cohort (n = 1,085,291), we did not find significant heterogeneity in Long COVID risk during the seven months following vaccination. Interpretation We found that COVID-19 vaccination was protective against Long COVID, and we did not observe a significant waning of this protection within seven months after vaccination. Funding This research was financially supported by the 10.13039/100000060National Institute of Allergy and Infectious Diseases (1K01AI182501 to Zachary Butzin-Dozier) and a Global Development grant (OPP1165144) from the 10.13039/100000865Bill & Melinda Gates Foundation to the 10.13039/100005595University of California, Berkeley, CA, USA. Individual authors were supported by the following funding sources: 10.13039/100000025NIMHR01131542 (PI Rena C. Patel), Jerrod Anzalone is supported by the 10.13039/100000057National Institute of General Medical Sciences, U54 GM115458, which funds the Great Plains IDeA-CTR Network. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
Z. Butzin-Dozier, Yunwen Ji, Lin-Chiun Wang et al.· EBioMedicine· 0 citations
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