# Interpretable deep learning models for translational medicine

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2021 · $309,404

## Abstract

Understanding the state of cellular signaling systems provides insights to how cells behave under physiological
and pathological conditions. Cellular signaling systems are organized as hierarchy (cascade) and signals of a
molecular is often compositionally encoded to control cellular processes, such as gene expression. This
project aims to develop advanced deep learning models (DLMs) to simulate cellular signaling systems based
on gene expression data. In last 3 years, the project has made significant progresses, but the challenges
remain. Importantly, contemporary DLMs behave as “black boxes”, in that it is difficult to interpret how signals
are encoded and how to interpret which signal a hidden node represent in a DLM. This black-box nature
prevents researchers from gaining biological insights using DLMs, even though these models can be much
superior in modeling data than other types of models in many tasks, e.g., predicting drug sensitivity of cancer
cells. In this competitive renewal, we propose to develop novel DLMs and innovative inference algorithms to
train “interpretable” DLMs and apply them in translational research. The proposed research is innovative and
of high significance in several perspectives: 1) Our novel DLMs and algorithms take advantage of big data
resulting from systematic chemical/genetic perturbations of cellular signaling machinery, so that we can use
the perturbation condition as side information to reveal how signals are encoded in a DLM. 2) We integrate
principles of causal inference and information theory with deep learning method to make DLMs interpretable.
As results, that researchers can gain mechanistic insights from such models. 3) Innovative application of
interpretable DLMs will advance translational research. For example, we will train interpretable DLMs to model
cellular signaling at the level of single cells and use this information investigate inter-cellular interactions
among cells in tumor microenvironment to shed light on immune evasion mechanisms of cancers. We will also
use information derived from interpretable DLMs to predict cancer cell drug sensitivity. We anticipate that our
study will bring forth significant advances not only in deep learning methodology but also in precision medicine.

## Key facts

- **NIH application ID:** 10171908
- **Project number:** 5R01LM012011-06
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** XINGHUA LU
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $309,404
- **Award type:** 5
- **Project period:** 2015-04-01 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10171908

## Citation

> US National Institutes of Health, RePORTER application 10171908, Interpretable deep learning models for translational medicine (5R01LM012011-06). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10171908. Licensed CC0.

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