# Unlocking complex co-expression network using graphical models

> **NIH NIH R01** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2020 · $400,001

## Abstract

Many chronic diseases are complex and very heterogeneous. They can be affected by multiple genes in
combination with lifestyle and environmental factors, and patients of one disease can be divided into
subgroups, e.g., cancer subtypes or stages of Alzheimer's disease (AD). One can use the genome-wide
gene expression data to investigate these disease's molecular signatures, which may help understand
disease etiology and guide precise treatments. Graphical models are powerful tools to estimate complex
network interactions among a large number of genes. To develop biostatistical and machine learning
methods to estimate such directed graphical models using gene expression data is the primary goal of this
project. To this end, this project contains the following research activities: (1) Given known disease subtypes,
Aim 1 develops novel techniques to jointly estimate multiple undirected/directed graphical models with one
model per subtype. (2) In Aim 2, we consider the situation where disease subtypes are not defined a prior.
We propose to identify disease subtypes by gene expression clustering, and the uncertainty of clustering is
incorporated into the estimation of multiple directed graphical models in Aim 1. (3) Recent single cell RNAsequencing
technology enables researchers to profile multiple cells from the same patient. Aim 3 focuses on
estimating multiple directed graphical models (e.g., for multiple subclones of tumor cells, or multiple types of
brain cells) using single cell RNA-seq data of one patient. The effectiveness of the proposed graphical model
estimation methods will be demonstrated using cancer and AD data analysis. The research results have
great potential to offer new insights on the understanding and precise treatments of these diseases.
Furthermore, these methods are general enough to be applied to analyze omic data of other diseases as
well. The research team will disseminate computational efficient and user-friendly software packages,
research publications, academic presentations and collaborations with experts in cancer research and
neurological diseases.

## Key facts

- **NIH application ID:** 9979887
- **Project number:** 5R01GM126550-04
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** YUFENG LIU
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $400,001
- **Award type:** 5
- **Project period:** 2017-08-15 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9979887

## Citation

> US National Institutes of Health, RePORTER application 9979887, Unlocking complex co-expression network using graphical models (5R01GM126550-04). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/9979887. Licensed CC0.

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