# Novel computational approaches for pharmacogenomics of complex diseases

> **NIH NIH R35** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2024 · $385,700

## Abstract

Summary/Abstract
Developing better therapies for complex diseases necessitates comprehensive understanding of intricate
pharmacogenomic mechanisms. The explosion of multi-omic data and biomedical literature has enabled
systematic explorations in pharmacogenomics; however, it is accompanied by substantial computational hurdles.
Addressing this challenge, the PI’s laboratory has been pioneering state-of-the-art machine and deep learning
models that comprehensively integrate diverse types of biomedical data to study disease biology, optimize
treatment strategies, and ultimately enhance patient outcomes. We successfully applied our computational
frameworks to diseases such as cancer, autoimmune diseases, hematopoietic disorders, and viral infections,
yielding biologically meaningful insights. Over the forthcoming five years, the R35 award will augment the breadth
and depth of our endeavors through three distinct yet synergistic themes: 1) predicting effects of therapies on
diseased cells, 2) inferring pharmacogenomic interactions between genes and drugs, and 3) developing
accessible computational resources. Specifically, Theme 1 will devise advanced deep learning models that
integrate multi-omic information – ranging from genetics to transcriptomics and proteomics – to predict the
molecular effects (e.g., inhibition of critical genes or pathogenic pathways) and phenotypic responses
(suppression of cell activation, viability, etc.) induced by various genetic and chemical perturbations in disease
models. By leveraging the emerging large language models, Theme 2 will dissect an extensive corpus of
published literature to construct the landscape of pharmacogenomic gene–drug interactions. These interactions
will illuminate the mechanisms of actions and molecular intricacies that govern treatment efficacy in the context
of diseases. Theme 3 will create accessible computational resources that empower the utilization of cutting-edge
computational methods and emerging genomic/pharmacogenomic profiling technologies. Completion of the
proposed research will establish resources that facilitate cost-effective prioritization of therapeutic targets and
agents for follow-up biological and clinical investigations, and evidence-based strategies for drug repositioning.
Our research is innovative as it formulates a sophisticated computational framework that integrates deep learning
machineries tailored to individual data modalities. The accessible tools will promote FAIR-ness (Findability,
Accessibility, Interoperability, and Reusability) of relevant data. The framework established through this project
is adaptable to computational methodologies and profiling technologies arising in the future, and broadly
applicable across complex diseases. The PI is uniquely suited to lead the proposed research for his
transdisciplinary experience in bioinformatics, engineering, and biomedicine, along with synergistic
collaborations with wet-lab and clinical scientists in a vibrant tr...

## Key facts

- **NIH application ID:** 10937188
- **Project number:** 1R35GM154967-01
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Yu-Chiao Chiu
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $385,700
- **Award type:** 1
- **Project period:** 2024-09-01 → 2029-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10937188

## Citation

> US National Institutes of Health, RePORTER application 10937188, Novel computational approaches for pharmacogenomics of complex diseases (1R35GM154967-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10937188. Licensed CC0.

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