Author

Julien Vibert

InsermORCID 0000-0002-2370-3302Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Cancer Biology· 1 Immunology· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Research Focus:differential expression· 2 Approach:experimental assay· 2 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Research Focus:drug response / drug discovery· 1 Research Focus:gene regulatory network inference· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Agathe PeltieriD· 1 shared Alicia Tran-DieniD· 1 shared Amaury LerusteiD· 1 shared Amélie RoehrigiD· 1 shared Ana Inés LalanneiD· 1 shared Andreas MöckiD· 1 shared Aurélie DarboisiD· 1 shared Benjamin BesseiD· 1 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 16 mutual co-author links

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1 shared paperCancer Biology

Agathe PeltierORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Specific killing of Ewing sarcoma by TCR-T cells targeting public neogene-encoded antigens

Ana Inés Lalanne, Céline Collin, Floriane Petit, Mégane Lacaud, Yago A. Arribas, Aurélie Darbois, Amaury Leruste, Maria K. Koshkina, Kyle A. Raymond, Paul Klein, Julien Vibert, Sakina Zaïdi, Sandrine Grossetête, Jill Pilet, Karine Laud-Duval, , Olivier Delattre

The paper demonstrates that TCR-T cells targeting neogene-encoded antigens can specifically kill Ewing sarcoma cells in vitro and in vivo.

bioRxiv · Jun 25, 2026 · PreprintImmunology

Single-cell trajectories in metastatic urothelial carcinoma reveal tumor-immune reprogramming and macrophage-driven resistance to PD-(L)1 blockade

Ronan Flippot, Amélie Roehrig, Julien Vibert, Nicolas Stransky, Luc Cabel, Kevin Mulder, Benjamin Besse, Claudio Nicotra, Maud Ngo Camus, Christophe Massard, Étienne Rouleau, Gérôme Jules-Clément, Alicia Tran-Dien, Lambros Tselikas, Constance Thibault, , Yohann Loriot

This study uses single-cell RNA sequencing to analyze metastatic urothelial carcinoma and identifies macrophage-driven resistance to PD-(L)1 blockade as a key mechanism.

bioRxiv · Apr 2, 2026 · PreprintCancer Biology

End-to-end deep learning versus machine learning for biomarker discovery in cancer genomes

Michaela Unger, Chiara Maria Lavinia Loeffler, Julien Vibert, Laura Žigutytė, Srividhya Sainath, Tim Lenz, Andreas Möck, Stefan Fröhling, Trevor A. Graham, Zunamys Itzell Carrero, Jakob Nikolas Kather

This study compares deep learning and machine learning for biomarker prediction in cancer genomes, finding DL performs better in cross-assay and data-sparse settings.

bioRxiv · Jan 6, 2025 · PreprintBioinformatics