Author

Madison R. Jenner

University of North Carolina at Chapel HillORCID 0000-0002-3402-8509Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Research Focus:drug response / drug discovery· 2 Organism:human· 2 Assay:proteomics· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Research Focus:differential expression· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Brian T. Golitz· 2 shared Chinmaya U. JoisaiD· 2 shared Jen Jen YehiD· 2 shared Matthew E. BerginskiiD· 2 shared Shawn M. GomeziD· 2 shared Gabriela Herrera Loeza· 1 shared Gary L. JohnsoniD· 1 shared John R Leary· 1 shared

Activity by Year

2021-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 39 mutual co-author links

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2 shared papersBioinformatics

Brian T. Golitz

Mutual co-authors in cluster

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2 papers

Recent·Most cited

BioinformaticsPeerJPeer-reviewedNov 16, 2023

Integrated Single-Dose Kinome Profiling Data is Predictive of Cancer Cell Line Sensitivity to Kinase Inhibitors

Chinmaya U. Joisa, Kevin A. Chen, Matthew E. Berginski…·Shawn M. Gomez

The paper links kinase inhibitor treatment effects to cell line responses using integrated kinome profiling data and builds predictive models with high accuracy.

6 citations

humanproteomics

BioinformaticsbioRxivPreprintJul 21, 2021

Kinome state is predictive of cell viability in pancreatic cancer tumor and stroma cell lines

Matthew E. Berginski, Madison R. Jenner, Chinmaya U. Joisa…·Shawn M. Gomez

The paper links kinome state with cell viability in PDAC tumor and stroma cell lines using regression and classification models, finding predictive relationships and key kinases driving viability responses.

6 citations

humanproteomics