Author

Giulia Crocioni

Netherlands eScience CenterORCID 0000-0002-0823-0121Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Immunology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:protein structure prediction· 2 Approach:contrastive learning· 1 Organism:human· 1 Assay:protein structure· 1 Assay:proteomics· 1 Approach:transformer / foundation model· 1

Frequent Co-Authors

Daniel T. RademakeriD· 2 shared Li C. XueiD· 2 shared Amy L. KessleriD· 1 shared ChiaYu Lin· 1 shared Coos A. B. BaakmaniD· 1 shared Cunliang GengiD· 1 shared Dani L. BodoriD· 1 shared Daniil LepikhoviD· 1 shared

Activity by Year

2023-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

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2 shared papersImmunology

Daniel T. RademakerORCID

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2 papers

Recent·Most cited

ImmunologybioRxivPreprintJan 20, 2025

SwiftMHC: A High-Speed Attention Network for MHC-Bound Peptide Identification and 3D Modeling

Coos A. B. Baakman, Giulia Crocioni, Cunliang Geng…·Li C. Xue

SwiftMHC rapidly predicts peptide-MHC binding and generates 3D structures, outperforming sequence-based methods in speed while maintaining accuracy.

2 citations

humanproteomics

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BioinformaticsbioRxivPreprintDec 4, 2023

Improving generalizability for MHC-I binding peptide predictions through geometric deep learning

Dario F. Marzella, Giulia Crocioni, Tadija Radusinović…·Li C. Xue

This paper improves MHC-I binding peptide prediction generalizability using geometric deep learning, outperforming sequence-based methods without binding affinity data.

4 citations

protein structuredeep learning

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