# Integrating multi-omics datasets to infer phenotype-specific driver genes, regulatory interactions and drug response

> **NIH NIH R35** · UNIVERSITY OF NORTH TEXAS · 2020 · $149,597

## 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:** 10303256
- **Project number:** 7R35GM133657-03
- **Recipient organization:** UNIVERSITY OF NORTH TEXAS
- **Principal Investigator:** Serdar Bozdag
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $149,597
- **Award type:** 7
- **Project period:** 2019-08-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10303256

## Citation

> US National Institutes of Health, RePORTER application 10303256, Integrating multi-omics datasets to infer phenotype-specific driver genes, regulatory interactions and drug response (7R35GM133657-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10303256. Licensed CC0.

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