Understanding Complex Diseases with Advanced Data Tools
Statistical and Machine Learning Methods to Address Biomedical Challenges for Integrating Multi-view Data
This project creates new computer tools to combine different types of health information, helping us better understand complex diseases.
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 ID | NIH-11143170 on NIH RePORTER |
What this research studies
Many diseases are complicated, affecting different parts of the body and involving many factors like genetics and environment. Traditional ways of looking at just a few factors or one type of data often miss the full picture. This project develops new statistical and machine learning methods to bring together vast amounts of diverse health data, such as genetic, metabolic, and clinical information. By combining these different views, we hope to gain fresh insights into how complex diseases develop. This approach could help us find important biological markers that might lead to new treatments.
Who could benefit from this research
Good fit: Patients with complex, multi-system diseases may eventually benefit from the insights gained from this data analysis.
Not a fit: Patients seeking immediate new treatments or direct clinical intervention will not find that benefit from this foundational methods development.
Why it matters
Potential benefit: If successful, this work could lead to a deeper understanding of complex diseases and help identify new targets for future therapies.
How similar studies have performed: While integrating diverse data is a growing field, this project aims to develop novel, robust methods to overcome significant analytical challenges in this area.
Where this research is happening
Minneapolis, United States
- University of Minnesota — Minneapolis, United States (Active)
Researchers
- Principal investigator: Safo, Sandra E — University of Minnesota
- Study coordinator: Safo, Sandra E
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 below.