Author

Eliana Ruggiero

IRCCS Ospedale San RaffaeleORCID 0000-0002-2954-8278Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:deep learning· 1 Approach:experimental assay· 1 Organism:human· 1 Organism:mouse· 1 Assay:proteomics· 1 Research Focus:sequence-to-function modeling· 1

Frequent Co-Authors

Chiara BoniniiD· 2 shared Aaron ProdeusiD· 1 shared Allie Luther· 1 shared Birgit C. SchultesiD· 1 shared Dai LiuiD· 1 shared Eleni StampouloglouiD· 1 shared Enrica ZereikiD· 1 shared Evan F. LindiD· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 31 mutual co-author links

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2 shared papersSynthetic Biology

Chiara BoniniORCID

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2 papers

Recent·Most cited

Synthetic BiologybioRxivPreprintJul 28, 2026

Engineering antigen-driven co-stimulation and T helper cell activity into TCR-T cells with CD8-41BB fusion receptors enhances anti-tumor activity

Ivy Dutta, Justin Oh, Liz Cam…·Aaron Prodeus

This paper enhances TCR-T cell anti-tumor activity by engineering CD8-41BB fusion receptors, improving CD4+ and CD8+ T cell functions and tumor regression in mouse models.

DOI 10.64898/2026.07.27.739613

mouseexperimental assay

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BioinformaticsbioRxivPreprintMar 18, 2025

A Simple Generative Model for the Prediction of T-Cell Receptor - Peptide Binding in T-Cell Therapy for Cancer

Αλέξανδρος Παπανικολάου, Vladimir Sivtsov, Enrica Zereik…·Fabio Bonsignorio

This paper introduces a deep learning model called GRIP that predicts T-cell receptor epitope peptides, achieving high accuracy and generating novel sequences for cancer immunotherapy.

2 citations

humanproteomics

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