A tool that uses machine learning to curate and explore biomedical literature.
Official title: Continually Adaptive Machine Learning Platform for Personalized Biomedical Literature Curation and Exploration
Funded at University of Virginia · NIH-11080927 · Data updated 2026-11-12
This study is creating a user-friendly online tool that helps researchers easily find and understand the latest biomedical research by combining their own articles with a vast library of public studies, making it easier for them to stay updated and informed.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Virginia NIH-funded |
| Lab location | 1 site (Charlottesville, United States) |
| Project start | August 1, 2023 |
| Project end | April 30, 2027 |
| Funding year | 2025 |
| Award (this year) | $333,756 |
| NIH project number | 5R01LM014012-03 |
| Project ID | NIH-11080927 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are biomedical researchers and clinicians who require up-to-date literature for their work.
✕Less likely to help
Patients who are not involved in biomedical research or do not require access to scientific literature may not benefit from this research.
What this research studies
This research develops an innovative web-based platform that utilizes adaptive machine learning to continuously integrate and curate biomedical literature. It allows researchers to combine their own lab-generated articles with large public repositories, enabling personalized and context-specific literature analysis. The platform features a knowledge-enriched representation learning model that enhances the accuracy and efficiency of annotating biomedical articles and clinical records. Users can also integrate their own articles into the system, facilitating a dynamic exploration of the latest biomedical information.
Why it matters
Potential benefit: If successful, this research could significantly improve how researchers access and analyze biomedical literature, leading to more informed decision-making in clinical and research settings.
How similar studies have gone: Other research has shown success in utilizing machine learning for literature curation, indicating that this approach has potential for impactful outcomes.
Where this research is happening
-
University of Virginia
Charlottesville, United States Active
Researchers
- Principal investigator: Zhang, Aidong — University of Virginia
- Study coordinator: Zhang, Aidong
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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.