Author

Christine Surrette

GE Global Research (United States)ORCID 0000-0002-7334-4571Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Cancer Biology· 2 Bioinformatics· 1 Immunology· 1

Signature Assays, Organisms & Methods

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Organism:human· 4 Assay:imaging· 3 Approach:deep learning· 2 Research Focus:spatial domain detection· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:differential expression· 1 Approach:experimental assay· 1

Frequent Co-Authors

Fiona GintyiD· 4 shared J. Elizabeth McDonoughiD· 4 shared John F. GrafiD· 2 shared Alex D. CorwiniD· 1 shared Alison Caron· 1 shared Ana Guerreiro StückliniD· 1 shared Ananya BhallaiD· 1 shared Andrea Joseph De MicheliiD· 1 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 23 mutual co-author links

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4 shared papersCancer Biology

Fiona GintyORCID

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Author profile →

4 papers

Recent·Most cited

A neural network model delivers a highly prognostic protein signature in cancer stem cells that identifies relapse in stage III colorectal cancer patients.

Anna Matveeva, Sanghee Cho, Manuela Salvucci, Marc Sturrock, Joanna Fay, Tony O’Grady, Elizabeth McDonough, Christine Surrette, Jinru Shia, Canan Fırat, Nil Urgancı, Batuhan Kısakol, Emer O’Connell, John P. Burke, Niamh McCawley, , Jochen H.M. Prehn

This paper identifies a five-protein stem cell signature using a deep neural network that predicts relapse in stage III colorectal cancer patients.

bioRxiv · Mar 3, 2026 · PreprintCancer Biology

Integration of Multiomic and Multi-phenotypic Data Identifies Biological Pathways Associated with Physical Fitness

Azar Alizadeh, John F. Graf, Matthew J. Misner, Andrew Burns, Fiona Ginty, Kevin J. O’Donovan, J. Kenneth Wickiser, N Barringer, Gregory M Freisinger, Neil Hermansen, J. Elizabeth McDonough, Brian Davis, Evelina Loghin, Christine Surrette, Peter Tu, , Luca Marinelli

This paper introduces PhenoMol, a framework that integrates multiomic and multi-phenotypic data to identify biological pathways linked to physical fitness, outperforming traditional regression models.

Communications Biology · Feb 17, 2026Peer-reviewedBioinformatics

An end-to-end framework for Cell DIVE multiplexed imaging and spatial immune microenvironment analysis

Ananya Bhalla, Jonas Mackerodt, Mathilde Pohin, Sarah Hill, Jean-Baptiste Richard, Tom R Thomas, Romane Henninger, Fiona Ginty, Alex D. Corwin, Elizabeth McDonough, Christine Surrette, Kim S. Midwood, Ilya Korsunsky, Christopher Dominic Buckley, Dylan Windell, Mark Christopher Coles

bioRxiv · Jun 20, 2025 · PreprintImmunology

Mapping Tumor Microenvironment and Treatment Response of Diffuse Midline Glioma Using Multiplexed Immunofluorescence and AI Models

Sandra Laternser, Andrea Joseph De Micheli, Sarah C. Brüningk, Elizabeth McDonough, Antonela Petrović, Elisabeth Jane Rushing, Christine Surrette, Julia Bugajska, Augustine Eze, Lindsay Baker Kilburn, Luisa Machado, Susanne Dettwiler, Fabiola Prutek, Noëlia Casares, Daniel de la Nava, , Javad Nazarian

The paper uses multiplexed immunofluorescence and AI models to analyze the tumor microenvironment of diffuse midline glioma and predict antigens from H&E staining.

bioRxiv · Mar 23, 2025 · PreprintCancer Biology