Author

G. Croce

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Evolutionary Biology· 2 Immunology· 2

Signature Assays, Organisms & Methods

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Organism:mouse· 2 Assay:proteomics· 2 Approach:statistical model· 2 Research Focus:variant effect prediction· 2 Research Focus:cell type annotation· 1 Research Focus:cell-cell communication· 1 Organism:chicken / avian· 1 Assay:DNA methylation· 1

Frequent Co-Authors

D. Gfeller· 2 shared M. Weigt· 2 shared V. Zoete· 2 shared A. Harari· 1 shared A. Larabi· 1 shared B. Wolf· 1 shared C. Arber· 1 shared E. Ruppe· 1 shared

Activity by Year

2021-2022 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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2 shared papersImmunology

D. Gfeller

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

Rapid BCMA downmodulation on myeloma cells upon CAR T cell contact is mediated by trogocytosis and BCMA internalization

N. Camviel, B. Wolf, G. Croce, D. Gfeller, V. Zoete, C. Arber

This study designs APRIL-based dual-antigen CAR T cells for multiple myeloma and finds rapid BCMA downmodulation on tumor cells through trogocytosis and internalization, independent of binding moiety.

bioRxiv · Jul 23, 2022 · PreprintImmunology

Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes

J. Racle, P. Guillaume, J. Schmidt, J. Michaux, A. Larabi, K. Lau, M. A. S. Perez, G. Croce, R. Genolet, G. Coukos, V. Zoete, F. Pojer, M. Bassani-Sternberg, A. Harari, D. Gfeller

The paper predicts MHC-II epitope specificities using machine learning, revealing a reverse binding mode in ligands across multiple species.

bioRxiv · Jun 26, 2022Immunology

Deciphering polymorphism in 61,157 Escherichia coli genomes via epistatic sequence landscapes

L. Vigue, G. Croce, M. Petitjean, E. Ruppe, O. Tenaillon, M. Weigt

The paper predicts polymorphisms in 61,157 Escherichia coli genomes using context-aware models, showing genetic context constrains amino-acid changes in 30-50% of sites.

bioRxiv · Jan 21, 2022Evolutionary Biology

Epistatic models predict mutable sites in SARS-CoV-2 proteins and epitopes

J. Rodriguez-Rivas, G. Croce, M. Muscat, M. Weigt

The paper predicts mutable sites in SARS-CoV-2 proteins using epistatic models, showing improved accuracy over conservation profiles and identifying overrepresented mutations in variants of concern.

bioRxiv · Dec 11, 2021Evolutionary Biology