# Antibiotic Resistance Determination Utilizing Machine Learning

> **NIH NIH U01** · UT SOUTHWESTERN MEDICAL CENTER · 2022 · $459,223

## Abstract

Summary
Our long-term objective is to tackle antibiotic resistance by developing accurate and interpretable prediction
machine learning models that could be used clinically to speed up the care of patients with bacterial infections.
Current approaches to diagnosing bacterial infections rely on first culturing a pathogen from a collected specimen
followed by a variety of phenotypic tests to determine what antibiotic a particular bacterial isolate would be
sensitive or resistant to. This process can, in many cases, take days to finish. Developing an accurate way to
predict antibiotic resistance utilizing whole-genome sequencing data without the need for phenotypic testing is
the overall goal of this project. Our team applies the latest advances in deep learning and cloud computation.
We will pursue the following Specific Aims: 1) Curate a large dataset and develop a deep-learning prediction
model with state-of-the-art accuracies for a wide range of bacterial species and antibiotic combinations; 2)
Develop personalized machine learning models for chronic infections; 3) create open-sourced scalable user-
friendly resources for the broad research community. The successful completion of this work will provide a
paradigm shift in the way we diagnose bacterial infections and speed up the time to providing the correct
antibiotic for a specific pathogen.

## Key facts

- **NIH application ID:** 10442982
- **Project number:** 1U01AI169298-01
- **Recipient organization:** UT SOUTHWESTERN MEDICAL CENTER
- **Principal Investigator:** David Elihu Greenberg
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $459,223
- **Award type:** 1
- **Project period:** 2022-07-12 → 2025-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10442982

## Citation

> US National Institutes of Health, RePORTER application 10442982, Antibiotic Resistance Determination Utilizing Machine Learning (1U01AI169298-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10442982. Licensed CC0.

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