Author

M. Shaban

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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Cancer Biology· 2 Pathology· 1

Signature Assays, Organisms & Methods

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Approach:machine learning (classical)· 2 Assay:spatial transcriptomics· 2 Research Focus:cell type annotation· 1 Research Focus:cell-cell communication· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Organism:human· 1 Assay:imaging· 1

Frequent Co-Authors

F. Mahmood· 3 shared S. Jiang· 3 shared B. Zhu· 2 shared G. P. Nolan· 2 shared H. Chen· 2 shared H. Qiu· 2 shared J. Yeung· 2 shared M. A. Shipp· 2 shared

Activity by Year

2023-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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3 shared papersCancer Biology

F. Mahmood

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Author profile →

3 papers

Recent·Most cited

Epstein-Barr Virus Orchestrates Spatial Reorganization and Immunomodulation within the Classic Hodgkin Lymphoma Tumor Microenvironment

Y. Y. Yeo, H. Qiu, Y. Bai, B. Zhu, Y. Chang, J. Yeung, H. A. Michel, K. T. Wright, M. Shaban, S. Sadigh, D. Nkosi, V. Shanmugam, S. P. T. Yiu, P. Cramer, P. Rock, , S. Jiang

This study uses spatial multi-omics to compare EBV-positive and EBV-negative cHL, identifying distinct TME features and immunological signatures linked to EBV.

bioRxiv · Mar 5, 2024Cancer Biology

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

MAPS: Pathologist-level cell type annotation from tissue images through machine learning

M. Shaban, Y. Bai, H. Qiu, S. Mao, J. Yeung, Y. Y. Yeo, V. Shanmugam, V. Shanmugam, H. Chen, B. Zhu, G. P. Nolan, M. A. Shipp, S. J. Rodig, S. Jiang, F. Mahmood

MAPS provides pathologist-level cell type annotation from spatial proteomics data using machine learning, outperforming existing methods in speed and accuracy.

bioRxiv · Jun 25, 2023 · PreprintCancer Biology