# Mahoney - Proj 4

> **NIH NIH P20** · DARTMOUTH COLLEGE · 2020 · $237,862

## Abstract

PROJECT SUMMARY
The goal of this project is to develop network-based methods to predict tissue-specific pathways that underlie
diseases and drug responses. Successfully treating systemic diseases requires targeting diverse, tissue-
specific disease processes, which are not easy to measure directly. Internal organ biopsies are rare and
almost never taken in healthy subjects due to their inherent risks. Experiments probing disease states and drug
responses with high-throughput (HTP) gene expression have to mediate a tradeoff between the accessibility
and tractability of the assayed biological system and the direct translatability of results to target tissues. For
example, peripheral tissues such as skin and blood are easily acquired and typically contain important
information about the pathobiology of diseases, but HTP data in peripheral tissue is not a perfect surrogate for
HTP data in other tissues. Likewise, drug screening in cell culture allows for rapid and scalable determination
of gene-expression-response signatures to therapeutic compounds, but translating these results to target
tissues is not straightforward. The overarching goal of this project is to predict tissue-specific pathways from
easily obtained HTP data from outside that tissue. This project develops and validates a novel machine-
learning framework called “tissue network knowledge transfer” (TINKER), which predicts tissue-specific
pathways using HTP data from outside that tissue by mining tissue-specific gene-gene interaction networks.
TINKER will be used to predict differential gene expression in internal organs from HTP gene signatures
obtained from skin and blood from the same disease condition. TINKER will be tested by using it to predict
known drug targets in tissues from HTP gene signatures in cell culture. Finally, this project will systematically
optimize TINKER by incorporating nonlinear machine learning algorithms and network feature representations
that incorporate indirect connections among genes.

## Key facts

- **NIH application ID:** 9985948
- **Project number:** 5P20GM130454-02
- **Recipient organization:** DARTMOUTH COLLEGE
- **Principal Investigator:** John Matthew Mahoney
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $237,862
- **Award type:** 5
- **Project period:** — → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9985948

## Citation

> US National Institutes of Health, RePORTER application 9985948, Mahoney - Proj 4 (5P20GM130454-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9985948. Licensed CC0.

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