Improving data analysis in leukemia through advanced technology integration
Official title: Domain adaptation approaches to unify established and emerging sequencing technologies
Funded at University of Colorado Denver · NIH-11141430 · Data updated 2026-11-12
This study is looking at ways to improve how we understand and analyze data about acute myeloid leukemia (AML) by using smart computer techniques to fill in missing information, which could help doctors better predict treatment responses and identify important genetic clues, ultimately leading to better care for patients.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Colorado Denver NIH-funded |
| Lab location | 1 site (Aurora, UNITED STATES) |
| Project start | May 15, 2023 |
| Project end | July 31, 2027 |
| Funding year | 2024 |
| Award (this year) | $248,999 |
| NIH project number | 4R00HG012945-03 |
| Project ID | NIH-11141430 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include patients diagnosed with acute myeloid leukemia or related conditions who are undergoing treatment or monitoring.
✕Less likely to help
Patients with conditions unrelated to acute myeloid leukemia or those not undergoing any form of treatment may not receive benefits from this research.
What this research studies
This research focuses on enhancing the analysis of biological data related to acute myeloid leukemia (AML) by addressing the issue of missing data across different sequencing technologies. By employing machine learning techniques, specifically domain adaptation, the study aims to combine information from established and emerging technologies to fill in gaps in biological observations. This approach will be applied to various biomedical applications, including predicting responses to treatments and identifying genetic signatures from cell-free DNA. Patients may benefit from improved diagnostic and treatment strategies as a result of this research.
Why it matters
Potential benefit: If successful, this research could lead to more accurate diagnostics and personalized treatment options for patients with acute myeloid leukemia.
How similar studies have gone: Other research has shown promise in using machine learning techniques for data integration in biomedical applications, suggesting that this approach could be effective.
Where this research is happening
-
University of Colorado Denver
Aurora, United States Active
Researchers
- Principal investigator: Davidson, Natalie Rose — University of Colorado Denver
- Study coordinator: Davidson, Natalie Rose
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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.