# GeneSight: Advancing Insights into Complex Genetic Traits through CFDE-Enhanced Knowledge Graphs

> **NIH NIH R03** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2024 · $311,000

## Abstract

Abstract:
Unraveling the molecular mechanisms that link SNPs and genes identified in GWAS studies to
the disease is a challenge that must be overcome to translate these genetic discoveries into
actionable health insights. We would like to build a machine learning tool on the basis of visible
neural networks (vNN) that recently showed success to provide predictive and explanatory
power on cellular responses to gene regulations or disease treatment. Key to the vNN approach
is an understandable network such as Knowledge Graph that contains rich annotations of
relevant entities and relationships among entities organized by integrative data sources. Our
hypothesis is that organizing and integrating data from the Common Fund Data Ecosystem
(CFDE) can enhance the explanatory power of vNN to illuminate GWAS results. We propose
combining the ROBOKOP Knowledge Graph with diverse biological data from the CFDE, and
vNN as a knowledge-based architecture to provide high interpretability in supervised learning.
ROBOKOP Knowledge Graph will serve as an organizational hub for integrating CFDE data
with existing knowledge. This query-able resource for CFDE data will be our first deliverable.
We will extract network-based relationships from this data and train vNN using genotypes and
phenotypes from T2D-focused GWAS, providing our second deliverable. The trained vNN, our
third deliverable, will enable the prediction of T2D phenotypes from genotype data. Lastly, we'll
provide the code base as a platform to expand this KG and vNN approach to other GWAS
studies and potentially be generalized for genome wide ‘omic analyses with large data sets.

## Key facts

- **NIH application ID:** 10990226
- **Project number:** 1R03OD038393-01
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Cheng-Han Chung
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $311,000
- **Award type:** 1
- **Project period:** 2024-09-05 → 2025-09-04

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10990226

## Citation

> US National Institutes of Health, RePORTER application 10990226, GeneSight: Advancing Insights into Complex Genetic Traits through CFDE-Enhanced Knowledge Graphs (1R03OD038393-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10990226. Licensed CC0.

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