Author

J. Nyman

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· 5 Immunology· 1

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Research Focus:cell type annotation· 4 Approach:deep learning· 3 Organism:human· 3 Assay:bulk RNA-seq· 2 Research Focus:cancer & tumor microenvironment· 2 Research Focus:differential expression· 2 Approach:experimental assay· 2 Assay:imaging· 2

Frequent Co-Authors

E. M. Van Allen· 4 shared D. A. Braun· 3 shared J. Rosenthal· 3 shared R. Umeton· 3 shared Z. Bakouny· 3 shared B. A. McGregor· 2 shared B. Jiang· 2 shared C. J. Wu· 2 shared

Activity by Year

2020-2023 · peak 4/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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4 shared papersCancer Biology

E. M. Van Allen

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Author profile →

6 papers

Recent·Most cited

Spatially aware deep learning reveals tumor heterogeneity patterns that encode distinct kidney cancer states

J. Nyman, T. Denize, Z. Bakouny, C. Labaki, B. M. Titchen, K. Bi, S. N. Hari, J. Rosenthal, N. Mehta, B. Jiang, B. Sharma, K. Felt, R. Umeton, D. A. Braun, S. Rodig, , E. M. Van Allen

The paper uses spatially aware deep learning to identify tumor heterogeneity patterns in kidney cancer, revealing distinct states associated with different histological subtypes.

bioRxiv · Jan 18, 2023Cancer Biology

Building tools for machine learning and artificial intelligence in cancer research: best practices and a case study with the PathML toolkit for computational pathology

J. Rosenthal, R. Carelli, M. Omar, D. Brundage, H. Ella, J. Nyman, S. Hari, E. M. Van Allen, M. Luigi, R. Umeton, M. Loda

The paper introduces PathML, a computational pathology toolkit designed for scalability, standardization, and ease of use in cancer research, with applications demonstrated across diverse use-cases.

bioRxiv · Oct 21, 2021Cancer Biology

Examining Batch Effect in Histopathology as a Distributionally Robust Optimization Problem

S. N. Hari, J. Nyman, N. Mehta, F. Dietlein, J. Rosenthal, B. Jiang, A. Chowdhury, H. Elmarakeby, E. Sengupta, R. Umeton, E. M. Van Allen

This paper shows that using abstention in a patch-based classifier improves performance in histopathology tasks while reducing reliance on spurious features.

bioRxiv · Sep 14, 2021 · PreprintCancer Biology

Integrative Clinical and Molecular Characterization of Translocation Renal Cell Carcinoma

Z. Bakouny, A. Sadagopan, P. Ravi, N. Y. Metaferia, J. Li, S. AbuHammad, S. Tang, T. Denize, E. R. Garner, X. Gao, D. A. Braun, L. Hirsch, J. A. Steinharter, G. Bouchard, E. Walton, , S. R. Viswanathan

This study characterizes translocation renal cell carcinoma (tRCC) through integrated clinical and molecular analysis of 152 patients, identifying key features like MiT/TFE fusions, NRF2-driven antioxidant response, and differential treatment outcomes.

bioRxiv · Apr 14, 2021Cancer Biology

Induction of a colitogenic phenotype in Th1 cells depends on IL-23R signaling

M. Pawlak, D. DeTomaso, G. Meyer zu Horste, Y. Lee, J. Nyman, D. Dionne, C. Wang, A. Wallrapp, P. R. Burkett, S. J. Riesenfeld, A. C. Anderson, A. Regev, R. J. Xavier, N. Yosef, V. K. Kuchroo

This study shows IL-23R signaling enables Th1 cells to acquire a colitogenic phenotype, distinct from Th17 cells, and identifies CD160 as a key regulator.

bioRxiv · Jan 24, 2021 · PreprintImmunology

Integrative Molecular Characterization of Sarcomatoid and Rhabdoid Renal Cell Carcinoma Reveals Determinants of Poor Prognosis and Response to Immune Checkpoint Inhibitors

Z. Bakouny, D. A. Braun, S. A. Shukla, W. Pan, X. Gao, Y. Hou, A. Flaifel, S. Tang, A. Bosma-Moody, M. X. He, N. Vokes, J. Nyman, W. Xie, A. H. Nassar, S. Abou Alaiwi, , T. H. Ho

This study characterizes the molecular features and immune profile of sarcomatoid and rhabdoid renal cell carcinoma, showing their responsiveness to immune checkpoint inhibitors and identifying key genetic and transcriptional determinants.

bioRxiv · May 28, 2020Cancer Biology