Author

Valérie Ducret

ORCID 0000-0001-5553-4091Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:human· 3 Assay:spatial transcriptomics· 3 Research Focus:cell-cell communication· 2 Research Focus:differential expression· 2 Approach:machine learning (classical)· 2 Assay:scRNA-seq· 2 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1

Frequent Co-Authors

Almudena Espín-PéreziD· 3 shared Caroline HoffmanniD· 3 shared Alberto RomagnoniiD· 2 shared Alex J. CornishiD· 2 shared Anaïs Chossegros· 2 shared Céline Thiriez· 2 shared Christian Esposito· 2 shared Elodie Pronier· 2 shared

Activity by Year

2025-2025 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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3 shared papersCancer Biology

Almudena Espín-PérezORCID

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3 papers

Recent·Most cited

Deciphering Cellular Ecosystems Driving Tumor Progression and Immune Escape from Spatial Transcriptomics and Single-Cell with COMPOTES

Loïc Herpin, Anaïs Chossegros, Roberta Codato, Josep Monserrat Sanchez, Jean El Khoury, Simon Grouard, Valérie Ducret, Alex J. Cornish, Baptiste Gross, Elodie Pronier, Caroline Hoffmann, Alberto Romagnoni, Eric Durand, Almudena Espín-Pérez, Quentin Bayard

The paper introduces a machine-learning method to identify recurrent cell-cell communication patterns across tumor samples using spatial transcriptomics and single-cell data, revealing key immune and genetic programs in bladder cancer.

bioRxiv · Oct 27, 2025 · PreprintBioinformatics

Multiple instance learning with spatial transcriptomics for interpretable patient-level predictions: application in glioblastoma

Simon Grouard, Christian Esposito, Jean El Khoury, Valérie Ducret, Céline Thiriez, Loïc Herpin, Anaïs Chossegros, Caroline Hoffmann, Quentin Bayard, Geneviève Robin, Nicole Tay, Esther Baena, Eric Y. Durand, Almudena Espín-Pérez, Lucas Fidon

This paper introduces SpaMIL, a spatial transcriptomics-based multiple instance learning framework for patient-level outcome prediction in glioblastoma, achieving high prognostic accuracy.

bioRxiv · Oct 13, 2025 · PreprintCancer Biology

Histology and spatial transcriptomic integration revealed infiltration zone with specific cell composition as a prognostic hotspot in glioblastoma

Lucas Fidon, Mélanie Lubrano, Caroline Hoffmann, Esther Baena, Céline Thiriez, Luis Cano, Alex J. Cornish, Elo Madissoon, Valérie Ducret, Christian Esposito, Auriane Riou, Eric Y. Durand, Benoît Schmauch, Elodie Pronier, Alberto Romagnoni, , Franck Bielle

The paper identifies a prognostic infiltration zone in glioblastoma using histology and spatial transcriptomic integration, revealing specific cell composition associated with long-term survival.

bioRxiv · Oct 8, 2025 · PreprintCancer Biology