Using advanced statistical models to analyze complex biological data for disease understanding
Official title: Interpretable Bayesian Non-linear statistical learning models for multi-omics data integration
Funded at University of Minnesota · NIH-10931642 · Data updated 2026-11-12
This study is looking at different types of biological information to help us understand complex diseases like cancer and heart problems better, and it’s for anyone interested in improving medical research and patient care through new tools and insights.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Minnesota NIH-funded |
| Lab location | 1 site (Minneapolis, United States) |
| Project start | September 20, 2023 |
| Project end | July 31, 2028 |
| Funding year | 2024 |
| Award (this year) | $374,672 |
| NIH project number | 5R35GM150537-02 |
| Project ID | NIH-10931642 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals diagnosed with cancer, cardiovascular diseases, or neurodegenerative disorders.
✕Less likely to help
Patients with non-complex or unrelated health conditions may not benefit from this research.
What this research studies
This research focuses on integrating various types of biological data, such as genomics and proteomics, to better understand complex diseases like cancer and cardiovascular disorders. By employing innovative Bayesian statistical methods, the project aims to uncover important biological markers and disease subtypes that can improve clinical predictions. The researchers will analyze large datasets from public and private sources to identify key pathways and molecules involved in these diseases. The ultimate goal is to develop user-friendly software that can assist in applying these methods to enhance medical research and patient care.
Why it matters
Potential benefit: If successful, this research could lead to improved diagnosis and treatment options for patients with complex diseases.
How similar studies have gone: Previous research has shown promise in using similar Bayesian approaches for analyzing multi-omics data, indicating potential for success in this novel application.
Where this research is happening
-
University of Minnesota
Minneapolis, United States Active
Researchers
- Principal investigator: Chekouo Tekougang, Thierry — University of Minnesota
- Study coordinator: Chekouo Tekougang, Thierry
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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.