Bringing older and newer DNA sequencing tests together
Domain adaptation approaches to unify established and emerging sequencing technologies
['FUNDING_OTHER'] · UNIVERSITY OF COLORADO DENVER · NIH-11196771
This project uses machine learning to fill in missing information across different DNA and cell sequencing tests to help understand autoimmune disease and blood cancer samples.
Quick facts
| Phase | ['FUNDING_OTHER'] |
|---|---|
| Study type | Nih_funding |
| Sex | All |
| Sponsor | UNIVERSITY OF COLORADO DENVER (nih funded) |
| Locations | 1 site (Aurora, UNITED STATES) |
| Trial ID | NIH-11196771 on ClinicalTrials.gov |
What this research studies
From my point of view, researchers are teaching computer models to recognize patterns in well-established sequencing tests and then use those lessons to predict what newer sequencing methods would have seen. They will apply this approach to three real problems: predicting how specific cell types respond in rheumatoid arthritis, finding the tissue source of cell-free DNA in blood, and identifying progenitor cell signals in blood cancers. The work mainly combines existing patient-derived datasets and computational methods so fewer samples and less invasive testing might be needed. This helps make newer technologies more useful even when some measurements are missing.
Who could benefit from this research
Good fit: People with autoimmune conditions like rheumatoid arthritis, patients with blood cancers such as AML, or volunteers willing to donate blood for cell-free DNA studies would be the most relevant participants.
Not a fit: People whose care does not involve sequencing tests or who cannot provide blood or tissue samples are unlikely to see direct benefits from this project.
Why it matters
Potential benefit: If this works, patients could get more useful information from newer sequencing tests without needing extra invasive samples, helping with diagnosis and monitoring.
How similar studies have performed: Some computational methods have successfully merged data across sequencing platforms before, but using domain-adaptation specifically for cfDNA tissue origin and cell-type response prediction is fairly new and not yet proven in clinics.
Where this research is happening
Aurora, UNITED STATES
- UNIVERSITY OF COLORADO DENVER — Aurora, UNITED STATES (ACTIVE)
Researchers
- Principal investigator: DAVIDSON, NATALIE ROSE — UNIVERSITY OF COLORADO DENVER
- Study coordinator: DAVIDSON, NATALIE ROSE
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.
Conditions: Autoimmune Diseases