Author

G. Zaki

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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Cell Biology· 1 Immunology· 1 Pathology· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Approach:deep learning· 2 Assay:imaging· 2 Assay:bulk RNA-seq· 1 Research Focus:cell-cell communication· 1 Research Focus:differential expression· 1 Approach:experimental assay· 1 Organism:human· 1

Frequent Co-Authors

A. Hara· 1 shared A. Hari· 1 shared A. Lee· 1 shared B. Ruf· 1 shared C. Allen· 1 shared C. Sahinalp· 1 shared F. Mahmood· 1 shared G. Khayrullina· 1 shared

Activity by Year

2020-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 18 mutual co-author links

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1 shared paperImmunology

A. Hara

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Deep Learning Model Imputes Missing Stains in Multiplex Images

M. Shaban, W. Lassoued, K. Canubas, S. Bailey, Y. Liu, C. Allen, J. Strauss, J. L. Gulley, S. Jiang, F. Mahmood, G. Zaki, H. A. Sater

The paper introduces a deep learning model, MAXIM, to impute missing protein markers in multiplex images, improving efficiency in staining and imaging processes.

bioRxiv · Nov 21, 2023 · PreprintPathology

MAIT cells have a negative impact on glioblastoma

S. keretsu, T. Hana, A. Lee, N. Kedei, N. Malik, H. Kim, J. Spurgeon, G. Khayrullina, B. Ruf, A. Hara, M. Coombs, M. Watowich, M. Watanabe, A. Hari, M. K. B. Ford, , M. Terabe

The paper identifies MAIT cells as immunosuppressive in glioblastoma, correlating their presence with poor patient survival and immune suppressive gene upregulation.

bioRxiv · Jul 17, 2022 · PreprintImmunology

A Deep Learning Pipeline for Nucleus Segmentation

G. Zaki, P. R. Gudla, K. Lee, J. Kim, L. Ozbun, S. Shachar, M. Gadkari, S. Jing, I. D. Fraser, L. M. Franco, T. Misteli, G. Pegoraro

This paper develops a deep learning pipeline to evaluate nuclear segmentation models trained on small, custom annotated datasets, showing they can match or exceed performance of pre-trained models.

bioRxiv · Apr 14, 2020Cell Biology