Author

Charles Joseph Vaske

ImmunityBio (United States)ORCID 0000-0001-8151-6612Unclaimed 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 Assay:ATAC-seq· 1 Research Focus:benchmarking· 1 Research Focus:cancer & tumor microenvironment· 1 Approach:deep learning· 1 Assay:DNA methylation· 1 Research Focus:gene regulatory network inference· 1 Approach:statistical model· 1

Frequent Co-Authors

Andliena TahiriiD· 1 shared Arvind Y.M. Sundaram· 1 shared Chandra L. TheesfeldiD· 1 shared Christopher W. SzetoiD· 1 shared Denise G O'MahonyiD· 1 shared Grethe Grenaker Alnæs· 1 shared Ina Skaara BrorsoniD· 1 shared Jeremi Sudol· 1 shared

Activity by Year

2019-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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1 shared paperCancer Biology

Andliena TahiriORCID

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2 papers

Recent·Most cited

Cancer BiologybioRxivPreprintSep 24, 2025

CLONAL SELECTION SUPPORTED BY SINGLE CELL DNA SEQUENCING REVEALS HORMONAL ADAPTATION AND RESISTANCE IN LOCALLY ADVANCED BREAST CANCER DURING NEOADJUVANT AROMATASE INHIBITION

Denise G O'Mahony, Tom Lesluyes, Ina Skaara Brorson…·Vessela Nedelcheva Kristensen

This study uses whole-exome sequencing and single-cell DNA sequencing to analyze subclonal evolution in breast cancer during aromatase inhibitor treatment, identifying resistance mechanisms and actionable targets.

DOI 10.1101/2025.09.24.678208

humanwhole-exome sequencing

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BioinformaticsiSciencePeer-reviewedSep 14, 2019

Deep learning with implicit handling of tissue-specific phenomena predicts tumor DNA accessibility and immune activity

Kamil Wnuk, Jeremi Sudol, Kevin Bijan Givechian…·Charles Joseph Vaske

The paper develops a deep learning model to predict DNA accessibility and immune activity in tumors using RNA-seq data, achieving high accuracy across diverse genomic regions and cohorts.

10 citations

humanDNA methylation

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