Author

Samantha Beville

University of North Carolina at Chapel Hill

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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Systems Biology· 1

Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 1 Research Focus:differential expression· 1 Organism:human· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Brian T. Golitz· 1 shared Chinmaya U. JoisaiD· 1 shared Denis O. OkumuiD· 1 shared Gary L. JohnsoniD· 1 shared Kevin A. CheniD· 1 shared Matthew E. BerginskiiD· 1 shared Shawn M. GomeziD· 1 shared Timothy Joseph StuhlmilleriD· 1 shared

Activity by Year

2023-2023 · peak 1/yr

Co-Author Collaboration Network

8 top collaborators · 28 mutual co-author links

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1 shared paperSystems Biology

Brian T. Golitz

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1 paper

Recent·Most cited

Combined kinome inhibition states are predictive of cancer cell line sensitivity to kinase inhibitor combination therapies

Chinmaya U. Joisa, Kevin A. Chen, Samantha Beville, Timothy Joseph Stuhlmiller, Matthew E. Berginski, Denis O. Okumu, Brian T. Golitz, Gary L. Johnson, Shawn M. Gomez

The paper predicts cancer cell line sensitivity to kinase inhibitor combinations using kinome inhibition states and machine learning, achieving high accuracy in both simulated and experimental settings.

bioRxiv · Aug 1, 2023 · PreprintSystems Biology