# Data-driven search of Common Fund data sets for better discoverability and novel meta-analysis

> **NIH NIH R03** · UNIVERSITY OF CALIFORNIA AT DAVIS · 2022 · $312,973

## Abstract

Project Summary
 NIH Common Fund (CF) programs have produced a number of unique and high-value data sets.
To solve complex biomedical questions, we need to find related data sets that can be co-analyzed for
specific study purposes. Many of the current search techniques depend on data descriptors which differ
across CF programs and may be incomplete or inaccurate. Many of these experiments output lists of
genes significant to certain biomedical conditions. We are proposing to use these gene lists to find
similar data sets. This approach will not only enable searching across CF data sets but also can connect
them to other experiments in other databases and biomedical catalogs, e.g., databases containing
disease-gene associations and molecular pathways. To achieve this aim, we will implement an efficient
linear algorithm to calculate similarities between large numbers of gene sets. Our prototype tool,
DBRetina, uses this algorithm to build huge similarity networks in few minutes using minimal
computational resources. DBRetina serves as the foundation for CurIndex, a study similarity graph
database that connects multiple health-related resources. DBRetina and CurIndex will allow advanced
search for related CF experiments and facilitate better interpretation of biomedical data.

## Key facts

- **NIH application ID:** 10577377
- **Project number:** 1R03OD034502-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA AT DAVIS
- **Principal Investigator:** Tamer Ahmed Mansour Ahmed
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $312,973
- **Award type:** 1
- **Project period:** 2022-09-20 → 2025-09-19

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10577377

## Citation

> US National Institutes of Health, RePORTER application 10577377, Data-driven search of Common Fund data sets for better discoverability and novel meta-analysis (1R03OD034502-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10577377. Licensed CC0.

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