# Lagrangian computational modeling for biomedical data science

> **NIH NIH R01** · UNIVERSITY OF VIRGINIA · 2020 · $360,227

## Abstract

The goal of the project is to develop a new mathematical and computational
modeling framework for from biomedical data extracted from biomedical
experiments such as voltages, spectra (e.g. mass, magnetic resonance,
impedance, optical absorption, …), microscopy or radiology images, gene
expression, and many others. Scientists who are looking to understand
relationships between different molecular and cellular measurements are often
faced with questions involving deciphering differences between different cell or
organ measurements. Current approaches (e.g. feature engineering and
classification, end-to-end neural networks) are often viewed as “black boxes,”
given their lack of connection to any biological mechanistic effects. The approach
we propose builds from the “ground up” an entirely new modeling framework
build based on recently developed invertible transformation. As such, it allows for
any machine learning model to be represented in original data space, allowing for
not only increased accuracy in prediction, but also direct visualization and
interpretation. Preliminary data including drug screening, modeling morphological
changes in cancer, cardiac image reconstruction, modeling subcellular
organization, and others are discussed.

## Key facts

- **NIH application ID:** 9874005
- **Project number:** 5R01GM130825-02
- **Recipient organization:** UNIVERSITY OF VIRGINIA
- **Principal Investigator:** Gustavo Kunde Rohde
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $360,227
- **Award type:** 5
- **Project period:** 2019-03-01 → 2022-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9874005

## Citation

> US National Institutes of Health, RePORTER application 9874005, Lagrangian computational modeling for biomedical data science (5R01GM130825-02). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/9874005. Licensed CC0.

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