Using machine learning to explore unknown chemicals in the body
Official title: A machine-learning platform to illuminate the chemical dark matter in mass spectrometry-based metabolomics
Funded at Princeton University · NIH-10910517 · Data updated 2026-11-12
This study is working on new computer tools to help scientists understand tiny molecules in our bodies that could affect our health, with the hope that this knowledge will lead to more personalized treatments for patients.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Princeton University NIH-funded |
| Lab location | 1 site (Princeton, UNITED STATES) |
| Project start | September 19, 2024 |
| Project end | July 31, 2029 |
| Funding year | 2024 |
| Award (this year) | $391,852 |
| NIH project number | 1DP5OD036960-01 |
| Project ID | NIH-10910517 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals with metabolic disorders or those interested in how their unique chemical makeup affects their health.
✕Less likely to help
Patients with well-defined metabolic profiles that are already fully understood may not receive significant benefits from this research.
What this research studies
This research focuses on developing advanced computational tools to analyze mass spectrometry data, which captures thousands of small molecules in the human body. By illuminating the so-called 'chemical dark matter,' the project aims to identify previously unknown metabolites that could play a role in health and disease. The approach combines cutting-edge machine learning techniques with metabolomics to enhance our understanding of how these chemicals affect human biology. Patients may benefit from insights gained through this research, which could lead to more personalized medical treatments based on individual metabolic profiles.
Why it matters
Potential benefit: If successful, this research could lead to breakthroughs in understanding diseases and developing personalized medicine strategies.
How similar studies have gone: Other research has shown promise in using computational tools to analyze complex biological data, suggesting that this approach could yield valuable insights.
Where this research is happening
-
Princeton University
Princeton, United States Active
Researchers
- Principal investigator: Skinnider, Michael Alexander — Princeton University
- Study coordinator: Skinnider, Michael Alexander
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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.