Author

Nicole Tay

Primary ORCID profile (0000-0002-1307-408X · 1 paper)Unclaimed ORCID profile · follow to get notified of new preprints

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Nicole TayiD1 paper · King's College London

Biology Disciplines

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Cancer Biology· 1

Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 1 Research Focus:cell-cell communication· 1 Assay:clinical cohort / trial· 1 Approach:contrastive learning· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1

Frequent Co-Authors

Almudena Espín-PéreziD· 1 shared Anaïs Chossegros· 1 shared Caroline HoffmanniD· 1 shared Céline Thiriez· 1 shared Christian Esposito· 1 shared Eric Y. DurandiD· 1 shared Esther BaenaiD· 1 shared Geneviève RobiniD· 1 shared

Activity by Year

2025-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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1 shared paperCancer Biology

Almudena Espín-PérezORCID

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1 paper

Recent·Most cited

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