Pathways to Digital Health: AI and Omics in Rheumatoid Arthritis
Unraveling the role of autoantibodies in rheumatoid arthritis: Toward personalized treatment
Explore how groundbreaking proteomic research is transforming our understanding of rheumatoid arthritis (RA). In this on-demand webinar, Allan Stensballe, PhD, shares new insights into the molecular landscape of RA-affected synovial tissue, revealing how autoantibodies and protein signatures may hold the key to more precise personalized therapies.
What you’ll learn:
- How integrated proteomic and immunological profiling uncovers distinct RA subtypes
- The role of autoantibodies in tracking disease progression and predicting treatment outcomes using KREX™ technology
- New molecular insights into RA pathology and therapeutic targeting opportunities
Whether you’re researching autoimmune mechanisms or exploring novel diagnostics and treatments, this session offers valuable perspectives from the frontlines of proteomics and precision medicine.
Watch now and take the next step in understanding RA at the molecular level.

Allan Stensballe, PhD
Aalborg University
Allan Stensballe is a distinguished researcher in the field of proteomics, focused on elucidating the molecular mechanisms underlying autoimmune disorders like RA. By integrating advanced proteomic technologies – such as nanoproteomics and protein array-based diagnostics – with clinical and immunological data, his pioneering work has significantly advanced personalized medicine by identifying disease biomarkers, exploring the role of the extracellular matrix in RA and informing targeted therapeutic strategies.
Pathways to Digital Health: AI and Omics in Rheumatoid Arthritis
A presentation by Allan Stensballe, PhD
More webinars
WebinarOpening the black box: Building and evaluating machine learning models for proteomics
Machine learning can be a powerful way to extract multivariate signals from high-dimensional proteomics data, but only when models are built and evaluated with rigor. In this on-demand webinar, explore a practical framework for developing predictive and explanatory models, with an emphasis on study design choices that help prevent data leakage, reduce overfitting, and improve reproducibility.
WebinarBeyond the trees: See the bigger biological picture with pathway enrichment analysis
Proteomic data holds enormous potential—but the biology is rarely contained in a single protein. When you’re handed a list of differentially abundant proteins, the next challenge is turning that list into a clear, testable story about mechanisms and processes.
WebinarFinding the signal: Identifying reproducible biomarkers in high-plex proteomics
In biomarker discovery, the challenge is rarely a lack of data. Rather, it is knowing how to separate meaningful biological signal from technical distraction. This webinar focuses on how to use univariate analysis as a practical and powerful entry point for biomarker discovery in high-plex proteomics studies.
