Using algorithms to reduce cancer health disparities caused by data inequality
Algorithm-based prevention and reduction of cancer health disparity arising from data inequality
This study is working to make sure that people from different ethnic backgrounds are better represented in cancer research, so that everyone can benefit from improved treatments and care.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Tennessee Health Sci Ctr NIH-funded |
| Lab location | 1 site (Memphis, United States) |
| Project ID | NIH-10892105 on NIH RePORTER |
What this research studies
This research focuses on addressing the significant data disadvantage faced by ethnic minority groups in cancer research. It aims to develop algorithm-based methods that can improve the representation of these groups in biomedical studies, particularly in cancer-related data. By analyzing clinical omics and genotype-phenotype data, the research seeks to identify conditions under which knowledge from majority populations can be effectively transferred to enhance machine learning outcomes for underrepresented populations. This approach could lead to more equitable healthcare solutions and better cancer treatment options for diverse ethnic groups.
Who could benefit from this research
Good fit: Ideal candidates for this research include individuals from ethnic minority groups who are at risk for or affected by cancer.
Not a fit: Patients who are not part of ethnic minority groups or those who do not have a history of cancer may not benefit from this research.
Why it matters
Potential benefit: If successful, this research could lead to improved cancer treatment and prevention strategies tailored to ethnic minority populations.
How similar studies have performed: Other research has shown promise in using data transfer techniques to improve outcomes for underrepresented populations, indicating that this approach could be effective.
Where this research is happening
Memphis, United States
- University of Tennessee Health Sci Ctr — Memphis, United States (Active)
Researchers
- Principal investigator: Cui, Yan — University of Tennessee Health Sci Ctr
- Study coordinator: Cui, Yan
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.