Predicting how chemicals affect human health using advanced algorithms
Official title: Improving chemical exposome target prediction by application of Coupled Matrix/Tensor-Matrix/Tensor Completion algorithms
Funded at University of Michigan at Ann Arbor · NIH-10897923 · Data updated 2026-11-12
This study is looking to help researchers and healthcare professionals understand how different chemicals in our environment affect our bodies by using smart computer programs to predict which parts of the body these chemicals impact.
Quick facts
| Grant type | Career grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Michigan at Ann Arbor NIH-funded |
| Lab location | 1 site (Ann Arbor, United States) |
| Project start | August 2, 2023 |
| Project end | July 31, 2025 |
| Funding year | 2024 |
| Award (this year) | $118,114 |
| NIH project number | 5K99ES034429-02 |
| Project ID | NIH-10897923 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals exposed to environmental chemicals or those concerned about the health impacts of such exposures.
✕Less likely to help
Patients who are not exposed to environmental chemicals or who do not have health concerns related to chemical exposure may not benefit from this research.
What this research studies
This research aims to improve our understanding of how various environmental chemicals interact with biological systems by developing advanced computational algorithms. By utilizing Coupled Matrix/Tensor-Matrix and Tensor Completion methods, the project seeks to predict the molecular targets and affected tissues of these chemicals on a large scale. The approach involves analyzing extensive biological data related to chemical exposures, optimizing algorithms for accuracy, and validating predictions through experimental methods. Ultimately, a web portal will be created to facilitate access to these predictions for researchers and healthcare professionals.
Why it matters
Potential benefit: If successful, this research could lead to better predictions of chemical impacts on health, potentially improving safety assessments and informing public health decisions.
How similar studies have gone: While the specific matrix completion methods proposed are novel, similar computational approaches have shown promise in predicting biological interactions in other contexts.
Where this research is happening
-
University of Michigan at Ann Arbor
Ann Arbor, United States Active
Researchers
- Principal investigator: Wang, Kai — University of Michigan at Ann Arbor
- Study coordinator: Wang, Kai
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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.