Author

Martin A. Cheever

Fred Hutch Cancer CenterORCID 0000-0002-6892-4141Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Assay:CITE-seq· 1 Research Focus:database / resource· 1 Research Focus:differential expression· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Assay:scRNA-seq· 1

Frequent Co-Authors

Candice D. ChurchiD· 1 shared Chihiro MorishimaiD· 1 shared Evan Greene· 1 shared Greg FinakiD· 1 shared Janis Marie TaubeiD· 1 shared Leonard D’AmicoiD· 1 shared Nina BhardwajiD· 1 shared Nirasha RamchurreniD· 1 shared

Activity by Year

2021-2021 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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1 shared paperBioinformatics

Candice D. ChurchORCID

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1 paper

Recent·Most cited

New interpretable machine learning method for single-cell data reveals correlates of clinical response to cancer immunotherapy

Evan Greene, Greg Finak, Leonard D’Amico, Nina Bhardwaj, Candice D. Church, Chihiro Morishima, Nirasha Ramchurren, Janis Marie Taube, Paul Nghiem, Martin A. Cheever, Steven P. Fling, Raphaël Gottardo

FAUST is a new interpretable machine learning method for single-cell cytometry that discovers and validates T cell correlates of response to anti-PD-1 immunotherapy.

Patterns · Oct 27, 2021Peer-reviewedBioinformatics