Author

M. D. Perry

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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Biophysics· 2 Pharmacology & Toxicology· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Assay:electrophysiology· 2 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Research Focus:drug response / drug discovery· 1 Approach:experimental assay· 1 Approach:machine learning (classical)· 1 Approach:statistical model· 1

Frequent Co-Authors

A. P. Hill· 3 shared J. I. Vandenberg· 3 shared M. J. Windley· 2 shared C. A. Ng· 1 shared J. A. Gillis· 1 shared J. Farr· 1 shared M. M. Mangala· 1 shared P. Young· 1 shared

Activity by Year

2019-2021 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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3 shared papersBiophysics

A. P. Hill

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3 papers

Recent·Most cited

Heterozygous KCNH2 variant phenotyping using Flp-In HEK293 and high-throughput automated patch clamp electrophysiology

C. A. Ng, J. Farr, P. Young, M. J. Windley, M. D. Perry, A. P. Hill, J. I. Vandenberg

This paper describes a high-throughput method to phenotype KCNH2 variants using automated patch-clamp electrophysiology and Flp-In HEK293 cells, enabling rapid assessment of variant function.

bioRxiv · Feb 2, 2021Biophysics

Co-expression of calcium channels and delayed rectifier potassium channels protects the heart from proarrhythmic events

S. Ballouz, M. M. Mangala, M. D. Perry, S. Heitmann, J. A. Gillis, A. P. Hill, J. I. Vandenberg

The paper identifies co-expression of CACNA1C and KCNH2 in cardiac cells as protective against proarrhythmic events through reduced action potential variability.

bioRxiv · Mar 11, 2020 · PreprintBiophysics

Protocol-dependent differences in IC50 values measured in hERG assays occur in a predictable way and can be used to quantify state preference of drug binding.

W. Lee, M. J. Windley, M. D. Perry, J. I. Vandenberg, A. P. Hill

This study shows that IC50 differences in hERG assays depend predictably on drug binding states and can be modeled using machine learning.

bioRxiv · Jan 30, 2019Pharmacology & Toxicology