Interpretable tools to combine multiple molecular tests for cancer and heart disease
Interpretable Bayesian Non-linear statistical learning models for multi-omics data integration
Researchers are creating easy-to-understand Bayesian models that blend genomics, proteomics, and other molecular tests to help people with cancer and cardiovascular disease get better predictions about their condition.
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-11194985 on NIH RePORTER |
What this research studies
If I have cancer or heart disease, this project aims to build new statistical tools that combine many types of molecular data (genes, proteins, epigenetics, and transcripts) to find important disease signals. The team will design interpretable Bayesian methods that can capture complex, non-linear relationships across these data types and point to key pathways and molecules. They will apply the methods to large public human datasets like TCGA, dbGaP, and GTEx and to partner-provided patient datasets to find disease subtypes and biomarkers. The researchers plan to release free, user-friendly software so clinicians and scientists can use the tools in future patient care and research.
Who could benefit from this research
Good fit: Ideal candidates are people with cancer or cardiovascular disease who have genomic, proteomic, or other molecular data available in research databases or through collaborating clinical sites.
Not a fit: Patients without molecular testing, without searchable clinical sample data, or with conditions unrelated to the studied molecular markers may not see direct benefit from this work.
Why it matters
Potential benefit: If successful, this work could identify clearer molecular subtypes and better biomarkers that help personalize diagnosis, prognosis, and treatment choices for cancer and heart disease patients.
How similar studies have performed: Previous multi-omics efforts have produced useful biomarkers and disease subtypes, but developing broadly interpretable Bayesian non-linear integration methods at this scale is relatively novel.
Where this research is happening
Minneapolis, United States
- University of Minnesota — Minneapolis, United States (Active)
Researchers
- Principal investigator: Chekouo Tekougang, Thierry — University of Minnesota
- Study coordinator: Chekouo Tekougang, Thierry
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.