Author

Federico Errica

NEC (Germany)ORCID 0000-0001-5181-2904Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Approach:deep learning· 1 Assay:proteomics· 1 Approach:statistical model· 1

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Anja MöschiD· 1 shared Filippo GrazioliiD· 1 shared Leonardo V. CastorinaiD· 1 shared Pierre MachartiD· 1 shared

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2025-2025 · peak 1/yr

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4 top collaborators · 6 mutual co-author links

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1 shared paperImmunology

Anja MöschORCID

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1 paper

Recent·Most cited

ImmunologyPLoS ONEPeer-reviewedMay 20, 2025

Assessing the Generalization Capabilities of TCR Binding Predictors via Peptide Distance Analysis

Leonardo V. Castorina, Filippo Grazioli, Pierre Machart…·Federico Errica

This paper evaluates how well TCR binding predictors generalize to unseen peptides by analyzing the distance between training and testing peptide distributions using sequence and 3D structure metrics.

11 citations

proteomicsdeep learning

𝕏 PostBluesky