Author

Evan Greene

Fred Hutch Cancer Center

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Research Focus:cell type annotation· 2 Assay:CITE-seq· 2 Research Focus:differential expression· 2 Organism:human· 2 Research Focus:database / resource· 1 Approach:deep learning· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Greg FinakiD· 2 shared Andrew McDavidiD· 1 shared Candice D. ChurchiD· 1 shared Chihiro MorishimaiD· 1 shared Denise AlleniD· 1 shared Fritz LekschasiD· 1 shared Janis Marie TaubeiD· 1 shared Leonard D’AmicoiD· 1 shared

Activity by Year

2021-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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2 shared papersBioinformatics

Greg FinakORCID

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2 papers

Recent·Most cited

Protein-based cell population discovery and annotation for CITE-seq data identifies cellular phenotypes associated with critical COVID-19 severity

Denise Allen, Matthew Weaver, S. V. Prokopchuk, Fritz Lekschas, Mike Jiang, Greg Finak, Evan Greene, Andrew McDavid

The paper applies a flow cytometry analysis tool to CITE-seq data to identify protein-based cell phenotypes linked to critical COVID-19 severity.

bioRxiv · Mar 14, 2024 · PreprintBioinformatics

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