# Integrating multiple biomedical data modalities to predict disease diagnosis

> **NIH NIH R35** · UNIVERSITY OF NORTH TEXAS · 2022 · $111,375

## Abstract

PROJECT SUMMARY
My lab’s research goal is to develop open source integrative computational tools that perform secondary
analysis of publicly available multi-omics biological, clinical and environmental exposure datasets to infer
context-specific regulatory interactions and modules, and to predict disease associated genes and
patient-specific drug response. With the recent advances in high-throughput technologies in biology, the
cost of data generation has reduced tremendously, which enabled the generation of vast amounts of
multi-omics datasets such as gene expression, microRNA expression, copy number alteration, and DNA
methylation. Numerous international and national consortiums have been established to generate these
multi-omics datasets to study regulatory elements in DNA, disease and healthy tissues, epigenetic
signatures, and drug responses. Furthermore, ongoing large initiatives such as UK Biobank, Million
Records Project, and the All of Us research program will bring vast amounts of multi-omics datasets from
millions of individuals. Consequently, there is a tremendous need for scalable methods that can integrate
different layers of multi-omics datasets across millions of individuals from different backgrounds. These
methods would produce valuable insights into human diseases and pave the way towards precision
medicine. My research program is devoted to utilizing these multi-omics datasets cost effectively by
developing open-source innovative and integrative computational resources. My lab has been successful
in developing open source integrative computational methods to integrate such datasets to infer gene
regulatory interactions and modules and to predict disease drivers. In the next five years, we aim to
extend our recent and ongoing work to infer context-specific regulatory interactions and modules, and to
predict disease associated genes and patient-specific drug response. We will integrate various types of
heterogenous multi-omics datasets to build integrative and scalable computational tools. The
computational tools we develop through this research will enable us to elucidate the genetic and
epigenetic architecture of regulatory interactions and drug response and discover novel disease
associated genes. Our tools will be applicable for any disease type and will enable researchers to
leverage publicly available multi-omics datasets to their full extent and pave the road towards precision
medicine. Through this research program, I will create research opportunities for graduate and
undergraduate students particularly those from under-represented groups.

## Key facts

- **NIH application ID:** 10660201
- **Project number:** 3R35GM133657-05S1
- **Recipient organization:** UNIVERSITY OF NORTH TEXAS
- **Principal Investigator:** Serdar Bozdag
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $111,375
- **Award type:** 3
- **Project period:** 2019-08-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10660201

## Citation

> US National Institutes of Health, RePORTER application 10660201, Integrating multiple biomedical data modalities to predict disease diagnosis (3R35GM133657-05S1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10660201. Licensed CC0.

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