Document Type
Poster Presentation
Publication Date
3-8-2026
Host
Gaylord National Harbor Resort and Convention Center
Conference/Symposium
American Society for Biochemistry and Molecular Biology (ASBMB) annual meeting
City/State
Oxon Hill, MD
Department
College of Arts and Sciences
Recommended Citation
Rivera-Mariani, F. E., Armina-Rodriguez, A., Campbell, T., & Solomon, E. (2026, March 7-10). Mechanism-aware ML identifies a Protease-Chemokine-Galectin (PCG) axis that links plasma proteomics to single-cell signaling and enables compact severity classification in COVID-19 [Poster presentation]. 2026 American Society for Biochemistry and Molecular Biology (ASBMB) Annual Meeting, Oxon Hill, MD, United States.
Comments
Funding Information: We gratefully acknowledge ImmPort (https://www.immport.org), funded by NIAID/NIH, for providing the immunological data used. Data supporting this publication are available under study accession SDY2129. We thank the original investigators for generating these valuable resources.
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Félix Rivera-Mariani, associate professor in the College of Arts and Sciences, presented research at the 2026 American Society for Biochemistry and Molecular Biology (ASBMB) annual meeting, highlighting a mechanism-aware machine learning approach to identify immune signaling drivers of COVID-19 severity. His study re-analyzed large multi-omic datasets and identified a Protease–Chemokine–Galectin signaling axis linking plasma proteomics to intracellular immune signaling, enabling accurate, interpretable classification of disease severity with a compact biomarker panel.
Rivera-Mariani conducted the research in collaboration with mentees, including Lynn University students and alumni, as well as a trainee from his alma mater, the Department of Microbiology and Immunology at the University of Puerto Rico School of Medicine (Medical Sciences Campus). This collaborative mentorship effort highlights the integration of computational biology, immunology and translational research to advance explainable artificial intelligence approaches for infectious disease and respiratory immunology research.