Author

Emmanuel S. Antonarakis

University of MinnesotaORCID 0000-0003-0031-9655Unclaimed ORCID profile · follow to get notified of new preprints

Additional preprints under this name without ORCID

Emmanuel S. Antonarakis1 paper · Johns Hopkins University

Biology Disciplines

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Cancer Biology· 1 Genomics· 1

Signature Assays, Organisms & Methods

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Organism:human· 2 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Approach:experimental assay· 1 Approach:machine learning (classical)· 1 Assay:proteomics· 1 Assay:whole-exome sequencing· 1

Frequent Co-Authors

Abderrahman DayiD· 1 shared Alec Horrmann· 1 shared Ali T. ArafaiD· 1 shared Anai KthariiD· 1 shared Anjishnu BanerjeeiD· 1 shared Ariel Ann NelsoniD· 1 shared Armand Bankhead· 1 shared Ben GeorgeiD· 1 shared

Activity by Year

2025-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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1 shared paperCancer Biology

Abderrahman DayORCID

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2 papers

Recent·Most cited

GenomicsbioRxivPreprintOct 8, 2025

Moving Beyond Binary Biomarkers: Machine Learning Model Resolves Concurrent and Molecularly Heterogeneous Mismatch Repair and Homologous Recombination Deficiencies in Prostate Cancer

Komal Sharma, Divin A. Wilson, Yu Wang…·Navonil De Sarkar

This paper develops a machine learning model to identify molecularly distinct DDR subgroups in prostate cancer, revealing concurrent repair deficiencies and improving therapeutic stratification.

DOI 10.1101/2025.10.08.680958

humanwhole-exome sequencing

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Cancer BiologyJournal of Extracellular VesiclesPeer-reviewedOct 1, 2025

Proteomic profiling of extracellular vesicles distinguishes prostate cancer molecular subtypes

Megan Ludwig, Ali T. Arafa, Saasha Vinoo…·Justin M. Drake

This study uses proteomic profiling of extracellular vesicles to distinguish prostate cancer subtypes and identify subtype-specific biomarkers.

4 citations

humanproteomics

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