Mechanistically informed deep learning to study autoreactive B cells in Myasthenia Gravis
Official title: Mechanistically informed deep learning to characterize autoreactive B cells in Myasthenia Gravis
Funded at Yale University · NIH-11366548 · Data updated 2026-11-12
A study using advanced computer models to find hidden immune-cell signals in people with Myasthenia Gravis, including those who don’t yet show detectable antibodies.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Yale University NIH-funded |
| Lab location | 1 site (New Haven, United States) |
| Project start | May 15, 2026 |
| Project end | April 30, 2031 |
| Funding year | 2026 |
| Award (this year) | $819,674 |
| NIH project number | 1R01AI189998-01A1 |
| Project ID | NIH-11366548 on NIH RePORTER |
Who could benefit from this research
✓Could help
People with Myasthenia Gravis, including seronegative patients, who have or may have autoimmune-related B cell activity.
✕Less likely to help
Patients without autoimmune B cell signatures or those not eligible for B cell–focused analyses may see limited direct benefit.
What this research studies
This project uses transformer-based deep learning to analyze B cell receptor sequences in Myasthenia Gravis patients, aiming to reveal patterns associated with autoimmunity. It combines computational modeling with experimental validation to identify biomarkers that may explain seronegative cases and guide future diagnostics. The goal is to develop interpretable models with uncertainty estimates to highlight biologically meaningful B cell features that could inform treatment decisions.
Why it matters
Potential benefit: If successful, it could lead to better diagnostic biomarkers and personalized approaches for MG patients, including those currently seronegative.
How similar studies have gone: Previous work shows that deep learning can uncover immune repertoire patterns, but applying interpretable models to MG biomarkers is still emerging.
Where this research is happening
-
Yale University
New Haven, United States Active
Researchers
- Principal investigator: Rodriguez Martinez, Maria — Yale University
- Study coordinator: Rodriguez Martinez, Maria
Related research
Other NIH-funded projects on the same topics.
-
Integrative histo-genomic analysis to guide cancer risk and targeted therapies with interpretable AI
Researchers are developing an advanced AI-powered approach to map genetic and cellular patterns across breast and cervical cancers to help tailor treatments.
-
Understanding how autoimmunity causes myasthenia gravis
This project looks at why the immune system attacks the nerve-muscle connection in myasthenia gravis and how different patient groups are affected.
-
Predicting how antimalarial drug resistance spreads with AI
A powerful AI model will be built to forecast how resistance to antimalarial drugs might spread in Africa and beyond, helping clinicians and public health teams stay ahead of outbreaks.
-
Self-supervised deep learning for biological sequences
A deep learning project uses self-supervised methods to better understand biological sequences and related data, with the goal of advancing biology and medicine.
-
Mobile phone imaging and AI to screen oral cancers in low-resource settings
A low-cost mobile phone device with artificial intelligence is being developed to help frontline health workers screen for oral cancer early in low-resource areas.
-
Linking Genetic Variants to Real-Life Health Effects with Smart AI
A new AI-powered approach will predict how genetic differences affect health across multiple levels to help doctors understand diseases and tailor care.
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.