Author

M. E. Burkard

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

Signature Assays, Organisms & Methods

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Organism:human· 3 Assay:bulk RNA-seq· 2 Approach:experimental assay· 2 Approach:Bayesian inference· 1 Research Focus:benchmarking· 1 Research Focus:cancer & tumor microenvironment· 1 Research Focus:cell type annotation· 1 Research Focus:chromatin & epigenetic regulation· 1

Frequent Co-Authors

A. R. Lynch· 2 shared A. S. Zhou· 2 shared B. A. Weaver· 2 shared A. A. Gillette· 1 shared A. H. Yeung· 1 shared A. K. DeZeeuw· 1 shared A. Sunil· 1 shared C. A. Pasch· 1 shared

Activity by Year

2021-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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2 shared papersCancer Biology

A. R. Lynch

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Author profile →

3 papers

Recent·Most cited

A survey of CIN measures across mechanistic models

A. R. Lynch, S. Bradford, A. S. Zhou, K. Oxendine, L. Henderson, V. L. Horner, B. A. Weaver, M. E. Burkard

This paper evaluates CIN measures across mechanistic models, finding that single-cell methods like scDNAseq outperform bulk methods and proposes a standardized unit for CIN measurement.

bioRxiv · Jun 15, 2023 · PreprintCancer Biology

Integrating Subclonal Response Heterogeneity to Define Cancer Organoid Therapeutic Sensitivity

J. D. Kratz, S. Rehman, K. A. Johnson, A. A. Gillette, A. Sunil, P. F. Favreau, C. A. Pasch, D. Miller, L. C. Zarling, A. H. Yeung, L. Clipson, S. J. Anderson, A. K. DeZeeuw, C. M. Sprackling, K. K. Lemmon, , D. A. Deming

The paper evaluates individual patient-derived cancer organoids using optical metabolic imaging to predict therapeutic response and identify subclonal populations and resistance mechanisms.

bioRxiv · Oct 15, 2021 · PreprintCancer Biology

Quantifying chromosomal instability from intratumoral karyotype diversity using agent-based modeling and Bayesian inference

A. R. Lynch, N. L. Arp, A. S. Zhou, B. A. Weaver, M. E. Burkard

This paper develops a framework to quantify chromosomal instability using agent-based modeling and Bayesian inference, validated with experimental data and applied to clinical samples.

bioRxiv · Apr 26, 2021Cancer Biology