# TR&D 3 - Network Guided Machine Learning

> **NIH NIH P41** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2022 · $223,091

## Abstract

TR&D 3: NETWORK GUIDED MACHINE LEARNING – PROJECT SUMMARY
Powerful machine learning techniques, including recent advances in deep learning, promise to revolutionize
our ability to predict biomedical outcomes and could one day replace people in tasks such as image analysis,
medical diagnosis, and precision therapy. However, most machine learning methods result in “black boxes”
which do not provide the mechanistic understanding needed to control and repair biological systems in
industrial and medical applications. Furthermore, machine learning models trained for high accuracy in one
context, such as predicting drug responses of cell lines, often transfer poorly to other contexts such as
predicting drug responses of patients. How can we gain both the predictive power of machine learning and the
interpretability and transferability of mechanistic models of biology? Here we explore a series of
complementary and innovative approaches to this question based on integrating machine learning models with
biological networks. Specifically, we aim to use networks to: [Aim 1] Guide the transfer of predictive models of
drug response from model systems to patients; [Aim 2] Apply machine learning models to genotype-phenotype
prediction in genome-wide association studies; and [Aim 3] Use machine learning for patient diagnosis and
clinical trial selection in precision medicine applications. These aims are motivated by Driving Biomedical
Projects focused on drug response prediction in cell lines and patients (Aim 1; DBPs 13,19), genome-wide
association analysis of disease (Aim 2; DBPs 14-15,17), predicting patient outcomes in cancer and major
depression (Aim 3; DBPs 8,16), and clinical trial design (Aim 3; DBP 18). Our methods will be made available
as open source software and on prominent cloud-based biomedical data and computing environments (TPs
6,7) to support wide adoption.
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## Key facts

- **NIH application ID:** 10401272
- **Project number:** 5P41GM103504-13
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Gary D. Bader
- **Activity code:** P41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $223,091
- **Award type:** 5
- **Project period:** 2010-09-13 → 2025-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10401272

## Citation

> US National Institutes of Health, RePORTER application 10401272, TR&D 3 - Network Guided Machine Learning (5P41GM103504-13). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10401272. Licensed CC0.

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