Author

Brian T. Golitz

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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Bioinformatics· 3 Systems Biology· 1

Signature Assays, Organisms & Methods

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Organism:human· 4 Research Focus:differential expression· 3 Approach:statistical model· 3 Assay:bulk RNA-seq· 2 Research Focus:drug response / drug discovery· 2 Approach:machine learning (classical)· 2 Assay:proteomics· 2 Research Focus:benchmarking· 1

Frequent Co-Authors

Chinmaya U. JoisaiD· 4 shared Matthew E. BerginskiiD· 4 shared Shawn M. GomeziD· 4 shared Gary L. JohnsoniD· 2 shared Jen Jen YehiD· 2 shared Kevin A. CheniD· 2 shared Madison R. JenneriD· 2 shared Denis O. OkumuiD· 1 shared

Activity by Year

2021-2023 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 38 mutual co-author links

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4 shared papersBioinformatics

Chinmaya U. JoisaORCID

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

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, Brian T. Golitz, Madison R. Jenner, Gabriela Herrera Loeza, Jen Jen Yeh, 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.

PeerJ · Nov 16, 2023Peer-reviewedBioinformatics

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

Kinome Inhibition States and Multiomics Data Enable Prediction of Cell Viability in Diverse Cancer Types

Matthew E. Berginski, Chinmaya U. Joisa, Brian T. Golitz, Shawn M. Gomez

The paper predicts cell viability in cancer cell lines using kinase inhibitor profiles and gene expression data, achieving R2 of 0.78 and RMSE of 0.154.

PLoS Computational Biology · Feb 21, 2023Peer-reviewedBioinformatics

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

Matthew E. Berginski, Madison R. Jenner, Chinmaya U. Joisa, Silvia G. Hererra, Brian T. Golitz, Matthew B. Lipner, John R Leary, Naim U. Rashid, Gary L. Johnson, Jen Jen Yeh, 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.

bioRxiv · Jul 21, 2021 · PreprintBioinformatics