Using machine learning to predict outcomes of bone marrow transplants
Machine learning with immunogenetics for the prediction of hematopoietic cell transplant outcomes
This study is looking at how using computer technology can help find the best donor matches for people with acute myelogenous leukemia who need a stem cell transplant, making the process safer and more effective by focusing on their unique genetic traits.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Sloan-Kettering Inst Can Research NIH-funded |
| Lab location | 1 site (New York, United States) |
| Project ID | NIH-10757366 on NIH RePORTER |
What this research studies
This research investigates how machine learning can be applied to immunogenetics to improve the success rates of hematopoietic cell transplantation (HCT) for patients with acute myelogenous leukemia (AML). By analyzing the genetic profiles of both donors and recipients, the study aims to identify specific genetic combinations that enhance the effectiveness of the transplant while minimizing the risk of complications such as graft-versus-host disease (GVHD). The approach involves integrating various genetic data to better understand how immune responses can be optimized in transplant scenarios. Patients may benefit from more personalized donor matching based on their unique genetic makeup.
Who could benefit from this research
Good fit: Ideal candidates for this research are patients diagnosed with acute myelogenous leukemia who are considering or are eligible for hematopoietic cell transplantation.
Not a fit: Patients with other types of cancers or those who are not candidates for bone marrow transplantation may not receive benefits from this research.
Why it matters
Potential benefit: If successful, this research could significantly improve the success rates of bone marrow transplants, leading to better outcomes for patients with leukemia.
How similar studies have performed: Previous studies have shown promising results using genetic profiling to predict transplant outcomes, indicating that this approach has potential for success.
Where this research is happening
New York, United States
- Sloan-Kettering Inst Can Research — New York, United States (Active)
Researchers
- Principal investigator: Hsu, Katharine C — Sloan-Kettering Inst Can Research
- Study coordinator: Hsu, Katharine C
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.