# Deep learning for understanding gene regulation in diseases via 'omics' integration

> **NIH NIH R35** · BROWN UNIVERSITY · 2024 · $380,575

## Abstract

PROJECT SUMMARY
We propose to develop and refine neural networks gene expression to understand gene
regulation in diseases. We will design deep learning frameworks to integrate various datasets
(histone modifications, 3D conformation, sequences, and SNPs) and model their relationship
with the gene expression. Our proposed models will explicitly capture the underlying structure
and complexity of the biological data to learn meaningful connections. For example, we will use
a graph-based neural network to model the 3D conformation of the DNA as a graph and learn
from the connections between different genomic regions to predict gene expression. One of our
critical goals for using these methods is to extract relevant signals that could be contributing to
the up- and down-regulation of genes. We will accomplish this goal by applying interpretation
methods for neural networks. These methods will allow us to assign importance scores to the
input features that contribute the most towards a particular prediction of interest. Comparing
these scores for genes across healthy and disease cell lines will provide insights into gene
misregulation and serve as a hypothesis driving tool for biological experiments. We also
propose a novel Bayesian inference-based interpretation method to improve explanations of
graph-based neural networks that could be applied to various tasks. Finally, given the
improvement of single-cell technologies and imputation methods, we will extend our deep
learning frameworks to model relationships between signals like chromatin accessibility and
DNA methylation with gene expression. This direction will allow us to explore the effectiveness
of the imputation methods in removing noise and generating high-quality single-cell samples for
usage in deep learning modeling of gene regulation. Looking at the modeled relationships
across the cell's developmental stages could pinpoint timepoints for potential misregulation in
diseases. Therefore, this proposal aims to develop unified approaches that utilize datasets
spanning multiple repositories to leverage their collective knowledge and improve our
understanding of diseases in a data-driven manner.

## Key facts

- **NIH application ID:** 10916192
- **Project number:** 5R35HG011939-04
- **Recipient organization:** BROWN UNIVERSITY
- **Principal Investigator:** Ritambhara Singh
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $380,575
- **Award type:** 5
- **Project period:** 2021-09-23 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10916192

## Citation

> US National Institutes of Health, RePORTER application 10916192, Deep learning for understanding gene regulation in diseases via 'omics' integration (5R35HG011939-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10916192. Licensed CC0.

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