# BRIDGE Center Standards Core

> **NIH NIH U54** · UNIVERSITY OF COLORADO DENVER · 2023 · $1,337,582

## Abstract

BRIDGE Center Standards Core Project Summary
AI offers great potential for the discovery of novel biomedical insights from linkages between disparate,
cross-domain datasets. Unfortunately, traditional hypothesis-driven datasets tend to be narrowly focused on
the targeted problem domain with little consideration to “AI-readiness”. To best enable the use of such datasets
in data-driven and cross-domain discovery, they must be made Findable, Accessible, Interoperable, and
Reusable (FAIR). Lack of FAIRness is particularly problematic for AI, which is data-hungry. To fully leverage the
power of AI approaches, researchers need to find and reuse data to combine into larger datasets, and the data
must be interoperable or harmonized to be combined meaningfully. Transforming pre-existing datasets into
AI-ready data is challenging, requiring extensive linking and curation by human experts. This challenge is
exacerbated when annotating and linking data across domains, where standards may be disparate in purpose
and specificity. Finally, many datasets do not adhere to best practices in data transparency, including content
attribution and conditions on distribution and reuse. These additional considerations of Traceability, Licensing,
and Connectedness create an operationalized model for FAIR: FAIR-TLC.
Overcoming the barriers to FAIR-TLC is key to translational science and AI-driven biomedical discovery. Our
team has led standards development efforts in numerous large consortia, including the GA4GH, HL7, and
N3C. Our standards for representing biomedical concepts have been widely adopted, including those for
human phenotypes (e.g., HPO, GA4GH Phenopackets), diseases (NCIt, Mondo, ICD-11), genes (Gene
Ontology), anatomy (Uberon), and molecular variation (GA4GH VRS). We have developed standards and tools
to address data provenance (SEPIO), contributions (Contributor Attribution Model), licensing barriers (Data
Use Ontology, Reusable Data Project), and connectivity (Linked data Model Language, LinkML).
We will build on our previous work, collaborative skills, and technical knowledge to develop a framework to
enable the harmonization of standards across biomedical domains. We will form working groups with
representatives of the Data Generation Projects (DGPs) to document use cases and synthesize data standard
requirements. We will provide protocols and training for specifying standards, and provide concierge services
in support of all deliverables and activities. We will create a version-controlled Bridge2AI Standards Registry to
inventory standards for use by the DGPs, specified in the modality-agnostic LinkML framework, discoverable
through the interactive Standards Hub, and automatically exportable to technical artifacts through our Data
Transformation Toolbox. We will build a Standards Evaluation Dashboard for assessment and discovery of
standards in datasets from Bridge2AI Data Generation Projects. We will promote best practices in the
transparent and respon...

## Key facts

- **NIH application ID:** 10661029
- **Project number:** 5U54HG012513-02
- **Recipient organization:** UNIVERSITY OF COLORADO DENVER
- **Principal Investigator:** Monica Cecilia Munoz-Torres
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $1,337,582
- **Award type:** 5
- **Project period:** 2022-07-06 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10661029

## Citation

> US National Institutes of Health, RePORTER application 10661029, BRIDGE Center Standards Core (5U54HG012513-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10661029. Licensed CC0.

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