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First data in RA - Cytodata 2025

Ize Buphamalai et al.

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First data in RA - Cytodata 2025

Author

Ize Buphamalai et al.

Date

Immune-mediated diseases are highly complex and heterogeneous, which makes the discovery of precision medicines difficult. In Graph’s most recent poster at CytoData 2025, our research team showcased our systematic approach to address this challenge. 🎯

By using rheumatoid arthritis (RA) as a case study, the Graph team demonstrates a generalizable framework that can work in iterative cycles of lab experimentation and prediction, i.e. a lab-in-the-loop approach 🔁, to systematically understand mechanisms and drivers of the disease. Key to the success of our approach are three main components:

  • We perturb viable cells directly from the patient, so called ex vivo models, which are a closer mirror of the disease than commonly used cell lines.

  • We integrate our findings using a knowledge engine that takes in known biology alongside our new data to narrow the search space of potential drug targets, as well as predict new promising targets.

  • We validate the predicted targets in the lab, contributing to our knowledge base and making future predictions even more accurate.

Check out the full poster here.

Immune-mediated diseases are highly complex and heterogeneous, which makes the discovery of precision medicines difficult. In Graph’s most recent poster at CytoData 2025, our research team showcased our systematic approach to address this challenge. 🎯

By using rheumatoid arthritis (RA) as a case study, the Graph team demonstrates a generalizable framework that can work in iterative cycles of lab experimentation and prediction, i.e. a lab-in-the-loop approach 🔁, to systematically understand mechanisms and drivers of the disease. Key to the success of our approach are three main components:

  • We perturb viable cells directly from the patient, so called ex vivo models, which are a closer mirror of the disease than commonly used cell lines.

  • We integrate our findings using a knowledge engine that takes in known biology alongside our new data to narrow the search space of potential drug targets, as well as predict new promising targets.

  • We validate the predicted targets in the lab, contributing to our knowledge base and making future predictions even more accurate.

Check out the full poster here.

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