Author

John F. Graf

GE Global Research (United States)ORCID 0000-0002-0396-9867Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Cancer Biology· 2 Bioinformatics· 1 Genomics· 1

Signature Assays, Organisms & Methods

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Organism:human· 4 Research Focus:differential expression· 2 Assay:imaging· 2 Research Focus:spatial domain detection· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1

Frequent Co-Authors

Fiona GintyiD· 4 shared J. Elizabeth McDonoughiD· 4 shared Sanghee ChoiD· 3 shared Anna MatveevaiD· 2 shared Batuhan KısakoliD· 2 shared Christine SurretteiD· 2 shared Daniel B. LongleyiD· 2 shared Deborah A. McNamaraiD· 2 shared

Activity by Year

2019-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 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

High-Resolution Spatial Proteomics Characterises Colorectal Cancer Consensus Molecular Subtypes

Batuhan Kısakol, Anna Sturrock, Sanghee Cho, Mohammadreza Azimi, Andreas Ulrich Lindner, Manuela Salvucci, Elizabeth McDonough, Joanna Fay, Tony O’Grady, Niamh McCawley, John Patrick Burke, Deborah A. McNamara, John F. Graf, Simon S. McDade, Daniel B. Longley, , Jochen H.M. Prehn

This study uses high-resolution spatial proteomics to characterize colorectal cancer consensus molecular subtypes at single-cell resolution, revealing subtype-specific immune and metabolic features.

bioRxiv · May 7, 2025 · PreprintCancer Biology

Tumor cell phenotype and heterogeneity differences in IDH1 mutant vs wild-type gliomas

Michael E. Berens, Anup Sood, Jill S. Barnholtz‐Sloan, John F. Graf, Sanghee Cho, Seungchan Kim, Jeffrey Kiefer, Sara Ann Byron, Rebecca F. Halperin, Sara Nasser, Jonathan R. Adkins, Lori Cuyugan, Karen Devine, Quinn T. Ostrom, Marta Couce, , Fiona Ginty

This study compares tumor cell phenotype and heterogeneity differences between IDH1 mutant and wild-type gliomas using integrated multi-omics data.

PLoS ONE · Dec 27, 2019Peer-reviewedGenomics