Using advanced statistical methods to analyze complex health data
Statistical and Machine Learning Methods to Address Biomedical Challenges for Integrating Multi-view Data
This study is working on new ways to combine and analyze different types of health information, like genetic data and medical records, to better understand complex diseases that affect multiple organs, which could lead to better treatments for patients like you.
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-10880557 on NIH RePORTER |
What this research studies
This research focuses on developing innovative statistical and machine learning techniques to integrate and analyze diverse health data from various sources, such as genomics and clinical records. By addressing the complexity and heterogeneity of diseases that affect multiple organs, the project aims to uncover new insights into disease mechanisms and identify potential therapeutic targets. Patients may benefit from improved understanding and treatment options for complex diseases through the integration of multi-view data.
Who could benefit from this research
Good fit: Ideal candidates for this research are individuals with complex, heterogeneous diseases that involve multiple biological and environmental factors.
Not a fit: Patients with simple, well-defined diseases that do not involve complex interactions may not benefit from this research.
Why it matters
Potential benefit: If successful, this research could lead to better diagnostic tools and targeted therapies for complex diseases.
How similar studies have performed: Other research has shown promise in using integrative approaches to analyze complex health data, indicating that this methodology has potential for success.
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.