Author

M. Hejna

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· 3 Biophysics· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Research Focus:cell type annotation· 2 Research Focus:differential expression· 2 Approach:experimental assay· 2 Assay:bulk RNA-seq· 1 Research Focus:chromatin & epigenetic regulation· 1 Approach:deep learning· 1 Assay:DNA methylation· 1

Frequent Co-Authors

J. S. Song· 2 shared R. L. Judson-Torres· 2 shared A. Basu· 1 shared A. H. Shain· 1 shared A. Jorapur· 1 shared A. Lex· 1 shared A. Ranjan· 1 shared B. C. Bastian· 1 shared

Activity by Year

2017-2020 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 13 mutual co-author links

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2 shared papersBiophysics

J. S. Song

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Author profile →

4 papers

Recent·Most cited

Bidirectional interconversion between mutually exclusive tumorigenic and drug-tolerant melanoma cell phenotypes

Y. Zhang, R. L. Belote, M. A. Urquijo, M. M. Hansen, M. Hejna, T. E. Moustafa, T. Liu, D. Lange, F. Vand-Rajabpour, M. Chang, B. K. Lohman, C. Stubben, X. Zhang, L. S. Weinberger, M. W. VanBrocklin, , R. L. Judson-Torres

This study identifies bidirectional interconversion between tumorigenic and drug-tolerant melanoma cell states through BRN2 expression, revealing distinct molecular programs and therapeutic implications.

bioRxiv · Aug 26, 2020 · PreprintCancer Biology

Measuring DNA mechanics on the genome scale

A. Basu, D. G. Bobrovnikov, Z. Qureshi, T. Kayikcioglu, T. T. Ngo, A. Ranjan, S. Eustermann, B. Cieza, M. Hejna, T. H. Rube, K.-P. Hopfner, C. Wolberger, J. S. Song, T. Ha

This paper develops loop-seq to measure DNA bendability genome-wide and finds sequence-encoded low bendability upstream of TSSs that influences nucleosome positioning and chromatin remodeling.

bioRxiv · Aug 17, 2020Biophysics

A machine-learning classifier trained with microRNA ratios to distinguish melanomas from nevi

R. Torres, U. E. Lang, M. Hejna, S. J. Shelton, N. M. Joseph, A. H. Shain, I. Yeh, M. L. Wei, M. C. Oldham, B. C. Bastian, R. L. Judson-Torres

This study develops a machine learning classifier using microRNA ratios to distinguish melanomas from nevi, achieving 83% sensitivity and 71-83% specificity across multiple datasets.

bioRxiv · Dec 27, 2018 · PreprintCancer Biology

High accuracy label-free classification of kinetic cell states from holographic cytometry

M. Hejna, A. Jorapur, J. S. Song, R. L. Judson

This paper develops a method to classify cell states from holographic cytometry data with high accuracy, tracking responses to cancer therapeutics over time.

bioRxiv · Apr 14, 2017Cancer Biology