A user-friendly machine-learning platform to reveal unknown metabolites in MS-based metabolomics
Official title: A machine-learning platform to illuminate the chemical dark matter in mass spectrometry-based metabolomics
Funded at Princeton University · NIH-11400086 · Data updated 2026-11-12
A Princeton-led project will create smart computer tools to identify unknown small molecules found in human samples using mass spectrometry, with potential benefits for health research.
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 | 2026 |
| Award (this year) | $410,000 |
| NIH project number | 5DP5OD036960-03 |
| Project ID | NIH-11400086 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates are individuals whose biological samples are analyzed by metabolomics in future studies or clinical settings and who contribute data or samples for research.
✕Less likely to help
People not undergoing metabolomics analysis or not involved in related research may not directly benefit.
What this research studies
This project develops advanced computational tools to interpret mass spectrometry data and uncover the 'dark matter' of the metabolome. By distinguishing real chemical signals from artefacts, the platform aims to improve our understanding of how molecules influence health and disease. The work will involve building and benchmarking AI models that can annotate chemical structures and pathways from complex data. Researchers and clinicians could eventually use these insights to personalize medicine and accelerate biomarker discovery. Participation would involve analysis of metabolomics data and collaboration with experts in chemistry and biology.
Why it matters
Potential benefit: If successful, patients could benefit from faster discovery of disease biomarkers and more personalized treatment strategies based on metabolite profiles.
How similar studies have gone: Previous metabolomics efforts have produced meaningful insights, but decoding the dark matter remains challenging and this approach is innovative.
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.