# Disease subtyping guided by clinical phenotype for precision medicine

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2024 · $304,983

## Abstract

Abstract
Unsupervised cluster analysis has been widely applied to omics data analysis for identifying molecular disease
subtypes, which may present distinct disease prognosis and/or unique underlying disease mechanism. The
findings can ultimately establish foundations for precision medicine. Existing disease subtyping methods in the
literature are, however, purely unsupervised. The identified disease subtypes are often irrelevant to clinical
outcome and cannot be translated into clinical practice. We hypothesize that an outcome-guided molecular
disease subtyping framework with systematic integration of multi-omics data, clinical information and biological
pathway knowledge will generate disease relevant and clinically actionable subtypes towards precision medicine.
The developed methods are expected to be applicable for a wide range of complex diseases, where disease
subtyping may be instrumental for finding novel therapeutic targets or improving treatment decisions. The
specific aims are: (1) Develop outcome-guided clustering (OG-Clust) framework for disease subtyping using a
single omics dataset. (2) Develop outcome-guided clustering (OG-Clust) framework for integrating multiple omics
datasets. (3) Application and validation in breast cancer and pediatric asthma.

## Key facts

- **NIH application ID:** 10775699
- **Project number:** 5R01LM014142-02
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** George C. Tseng
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $304,983
- **Award type:** 5
- **Project period:** 2023-02-03 → 2026-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10775699

## Citation

> US National Institutes of Health, RePORTER application 10775699, Disease subtyping guided by clinical phenotype for precision medicine (5R01LM014142-02). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10775699. Licensed CC0.

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