Author

Akash Patnaik

University of ChicagoORCID 0000-0001-5306-0208Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:human· 2 Research Focus:cell type annotation· 1 Research Focus:cell-cell communication· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Approach:experimental assay· 1 Research Focus:gene regulatory network inference· 1 Research Focus:host-pathogen & immune response· 1

Frequent Co-Authors

Brian William LabadieiD· 2 shared Kiranj Kishor ChaudagariD· 2 shared Aishwarya AtmakuriiD· 1 shared Alexander T. PearsoniD· 1 shared Ali AmiryousefiiD· 1 shared Amelie EchleiD· 1 shared Amy E. MoraniD· 1 shared Andreanne GagneiD· 1 shared

Activity by Year

2020-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 18 mutual co-author links

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2 shared papersCancer Biology

Brian William LabadieORCID

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3 papers

Recent·Most cited

Cancer BiologybioRxivPreprintSep 21, 2025

Spatial statistics for identifying and scoring immune clusters in high-plex profiles of primary prostate cancer

Ali Amiryousefi, Jeremiah A. Wala, Jia‐Ren Lin…·Peter Karl Sorger

The paper introduces statistical tools to analyze immune cluster patterns in prostate cancer and finds high-grade tumors show more immune infiltration and active immune features.

3 citations

humanspatial transcriptomics

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Cancer BiologybioRxivPreprintSep 15, 2023

Androgen blockade primes NLRP3 in macrophages to induce tumor phagocytosis

Kiranj Kishor Chaudagar, Srikrishnan Rameshbabu, Shenglin Mei…·Akash Patnaik

This study shows that androgen blockade increases NLRP3 in macrophages, which when combined with NLRP3 agonist, enhances cancer cell phagocytosis and tumor control in a mouse model of prostate cancer.

8 citations

mouseproteomics

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GeneticsNature CancerPeer-reviewedJul 27, 2020

Pan-cancer image-based detection of clinically actionable genetic alterations

Jakob Nikolas Kather, Lara Rosaline Heij, Heike Irmgard Grabsch…·Tom Luedde

This paper develops a deep learning method to predict genetic alterations from histological images across multiple cancer types.

670 citationstop 1%

humanimaging

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