Using AI to improve the understanding of genetic variants in cancer
Official title: Text Mining and Large Language Models for AI-Driven Evidence-Based Functional Annotation of Clinical Variants
Funded at Washington University · NIH-11124530 · Data updated 2026-11-12
This study is using smart computer technology to help make sense of cancer-related genetic information, so doctors and researchers can get better and faster insights without overwhelming human experts.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Washington University NIH-funded |
| Lab location | 1 site (Saint Louis, United States) |
| Project start | September 19, 2023 |
| Project end | August 31, 2028 |
| Funding year | 2024 |
| Award (this year) | $300,000 |
| NIH project number | 3U24CA275783-02S1 |
| Project ID | NIH-11124530 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are patients with genetic variants related to cancer who may benefit from improved diagnostic and treatment strategies.
✕Less likely to help
Patients without genetic variants or those not affected by cancer may not receive direct benefits from this research.
What this research studies
This research focuses on leveraging advanced AI technologies, specifically Large Language Models (LLMs), to enhance the curation of biomedical knowledge related to cancer. By automating the annotation and information extraction processes, the project aims to alleviate the workload on human curators, allowing for more efficient and accurate interpretation of genetic variants. The collaboration between the Clinical Interpretation of Variants in Cancer (CIViC) resource and UniProt will create innovative strategies that can be applied across various biomedical databases, ultimately improving the quality of information available to clinicians and researchers.
Why it matters
Potential benefit: If successful, this research could significantly enhance the accuracy and efficiency of genetic variant interpretation, leading to better-informed treatment decisions for cancer patients.
How similar studies have gone: Previous research has shown promise in using AI and NLP technologies for data curation, indicating that this approach could lead to significant advancements in the field.
Where this research is happening
-
Washington University
Saint Louis, United States Active
Researchers
- Principal investigator: Griffith, Malachi — Washington University
- Study coordinator: Griffith, Malachi
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.