This Project Grant award, identified as R03OD038393, was provided by the National Institute of Dental and Craniofacial Research (NIDCR) under the Oral Diseases and Disorders Research program (CFDA 93.121). The $1 award will fund the development of a machine learning tool using Visible Neural Networks (VNN) to enhance the explanatory power of knowledge graphs derived from data in the Common Fund Data Ecosystem (CFDE) for translating genome-wide association study (GWAS) results into actionable health insights, with a focus on type 2 diabetes. The key products to be delivered include: 1) a ROBOKOP knowledge graph integrating CFDE data, 2) a VNN model trained on GWAS genotype and phenotype data, and 3) the codebases to expand this approach to other GWAS and 'omic studies. The award period is from September 5, 2024 to September 4, 2025, and the work will be performed by the University of North Carolina at Chapel Hill.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $1 | 9/5/24 |