Using automated machine learning to understand how HIV interacts with addictive drugs
Official title: Knowledge-guided automated machine learning methods for modeling the interaction of HIV with addictive drugs
Funded at Cedars-Sinai Medical Center · NIH-11127698 · Data updated 2026-11-12
This study is working on new tools to help researchers understand how substance abuse and HIV affect health outcomes, making it easier for everyone to analyze important data and improve care for patients.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Cedars-Sinai Medical Center NIH-funded |
| Lab location | 1 site (Los Angeles, United States) |
| Project start | July 1, 2024 |
| Project end | May 31, 2029 |
| Funding year | 2025 |
| Award (this year) | $491,450 |
| NIH project number | 5R01LM014572-02 |
| Project ID | NIH-11127698 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals living with HIV who also have a history of substance abuse.
✕Less likely to help
Patients who do not have HIV or substance abuse issues may not benefit from this research.
What this research studies
This research aims to develop advanced data mining and analytics methods to help the substance abuse and HIV research communities better predict disease outcomes. By utilizing automated machine learning (AutoML), the project seeks to make complex data analysis accessible to all researchers, regardless of their technical expertise. The goal is to analyze multimodal data, including biomarkers related to substance abuse and HIV, to improve clinical decision-making and patient care. The research will leverage an open-source platform called Tree-Based Pipeline Optimization (TPOT) to streamline the process of selecting and optimizing machine learning algorithms for clinical data.
Why it matters
Potential benefit: If successful, this research could lead to improved prediction of HIV progression and better treatment strategies for patients with substance abuse issues.
How similar studies have gone: Other research has shown success in using machine learning approaches to analyze complex biomedical data, indicating that this method has potential for impactful results.
Where this research is happening
-
Cedars-Sinai Medical Center
Los Angeles, United States Active
Researchers
- Principal investigator: Moore, Jason H. — Cedars-Sinai Medical Center
- Study coordinator: Moore, Jason H.
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.