# Enhancing and making Biomarker Knowledge FAIR using contextual CFDE data

> **NIH NIH U24** · GEORGE WASHINGTON UNIVERSITY · 2024 · $975,028

## Abstract

The Common Fund (CF) research initiative has generated a wealth of data that can provide vital context,
origin, and, in some instances, quantitative inferences for biomarkers. However, the systematic harmonization
and organization of biomarker data, as well as their connections to CF data, remain in an early stage and is
currently the focus of the year-long Common Fund Data Ecosystem (CFDE) Biomarker-Partnership project that
aims to develop a working biomarker data model. The proposed BiomarkerKB project aims to refine and
populate the biomarker data model through a close and dynamic external partnership with the NCI-supported
Early Detection Research Network (EDRN) with built-in community input mechanisms. The initial focus is on
refining our current biomarker data model using EDRN's cancer biomarker data and knowledge. Initially
focusing on cancer will allow us to limit our scope while retaining the ability to evaluate a variety of data types
and therefore ensure extensibility of the model as new data types and technologies emerge. The Minimal
Viable Product (MVP) will include persistent biomarker identifiers, linked data, connections to recognized
standards and ontologies, downloads/APIs, and data access through interfaces for biomarker explorations.
This data model will serve as the cornerstone for AI-ready datasets, machine learning-based biomarker
prediction models, and biomarker knowledge graphs. The scientific use case the project proposes to support is
the ability to explore molecular biomarker-related knowledge for most prevalent cancers at a systems level,
categorized by biological functions through mapping to key ontologies, pathways, biomolecular data (glycans,
proteins, genes, metabolites) and Electronic Health Record (EHR) terms and tests. Example biomarkers
(including non-molecular ones that are of interest to Data Coordinating Centers (DCCs)) for other diseases will
also be considered to ensure the comprehensiveness and robustness of the data model. This project promises
to enrich our understanding of the translational health record and intervention space, revolutionizing the way
we approach diverse diseases, clinical assays, molecular mechanisms, and disease classifications. The
potential benefits extend to our partners in the EDRN and the broader CFDE community, underscoring the
real-world significance of biomarkers across the medical and research landscape.

## Key facts

- **NIH application ID:** 10994366
- **Project number:** 1U24OD038423-01
- **Recipient organization:** GEORGE WASHINGTON UNIVERSITY
- **Principal Investigator:** Raja Mazumder
- **Activity code:** U24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $975,028
- **Award type:** 1
- **Project period:** 2024-09-17 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10994366

## Citation

> US National Institutes of Health, RePORTER application 10994366, Enhancing and making Biomarker Knowledge FAIR using contextual CFDE data (1U24OD038423-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10994366. Licensed CC0.

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