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

Félix E. Rivera-Mariani; Albersy Armina-Rodriguez; Tashoy Campbell; Emily Solomon

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

Félix E. Rivera-Mariani^* (Presenter)
, Albersy Armina-Rodriguez
, Tashoy Campbell
, Emily Solomon

^*Corresponding author for this work

Biology

University of Puerto Rico - Medical Sciences Campus
Lynn University

Research output: Contribution to conference › Poster

1 Downloads (Pure)

Abstract

Original language	American English
State	Published - Mar 8 2026
Event	American Society for Biochemistry and Molecular Biology (ASBMB) Annual Meeting - Gaylord National Harbor Resort and Convention Center, Oxon Hill, United States Duration: Mar 7 2026 → Mar 10 2026

Conference

Conference	American Society for Biochemistry and Molecular Biology (ASBMB) Annual Meeting
Abbreviated title	ASBMD
Country/Territory	United States
City	Oxon Hill
Period	3/7/26 → 3/10/26

Bibliographical note

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.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 4 Quality Education

Access to Document

2026_CAS_Rivera-Mariani_asbmb2026Final published version, 80 KB

http://spiral.lynn.edu/facpubs/2373

Cite this

Rivera-Mariani, F. E., Armina-Rodriguez, A., Campbell, T., & Solomon, E. (2026). 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 session presented at American Society for Biochemistry and Molecular Biology (ASBMB) Annual Meeting, Oxon Hill, Maryland, United States. http://spiral.lynn.edu/facpubs/2373

Rivera-Mariani, Félix E. ; Armina-Rodriguez, Albersy ; Campbell, Tashoy et al. / 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 session presented at American Society for Biochemistry and Molecular Biology (ASBMB) Annual Meeting, Oxon Hill, Maryland, United States.

@conference{f105ee2b8b824665be55380771066e65,

title = "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",

author = "Rivera-Mariani, \{F{\'e}lix E.\} and Albersy Armina-Rodriguez and Tashoy Campbell and Emily Solomon",

note = "F{\'e}lix 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.; American Society for Biochemistry and Molecular Biology (ASBMB) Annual Meeting, ASBMD ; Conference date: 07-03-2026 Through 10-03-2026",

year = "2026",

month = mar,

day = "8",

language = "American English",

}

Rivera-Mariani, FE, Armina-Rodriguez, A, Campbell, T & Solomon, E 2026, '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', American Society for Biochemistry and Molecular Biology (ASBMB) Annual Meeting, Oxon Hill, United States, 3/7/26 - 3/10/26. <http://spiral.lynn.edu/facpubs/2373>

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. / Rivera-Mariani, Félix E. (Presenter); Armina-Rodriguez, Albersy; Campbell, Tashoy et al.
2026. Poster session presented at American Society for Biochemistry and Molecular Biology (ASBMB) Annual Meeting, Oxon Hill, Maryland, United States.

Research output: Contribution to conference › Poster

TY - CONF

T1 - 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

AU - Armina-Rodriguez, Albersy

AU - Campbell, Tashoy

AU - Solomon, Emily

A2 - Rivera-Mariani, Félix E.

N1 - 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.

PY - 2026/3/8

Y1 - 2026/3/8

M3 - Poster

T2 - American Society for Biochemistry and Molecular Biology (ASBMB) Annual Meeting

Y2 - 7 March 2026 through 10 March 2026

ER -

Rivera-Mariani FE, Armina-Rodriguez A, Campbell T, Solomon E. 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. 2026. Poster session presented at American Society for Biochemistry and Molecular Biology (ASBMB) Annual Meeting, Oxon Hill, Maryland, United States.