KSTAR: An algorithm to predict patient-specific kinase activities from phosphoproteomic data

Authors

Sam Crowl, University of Virginia
Ben T Jordan, University of Virginia
Hamza Ahmed, University of Virginia
Cynthia X Ma, Washington University School of Medicine in St. Louis
Kristen M Naegle, University of Virginia

Journal

Nature Communications

Publication Date

7-25-2022

Volume

13

Issue

1

First Page

4283

Document Type

Open Access Publication

DOI

10.1038/s41467-022-32017-5

Rights and Permissions

Crowl, S., Jordan, B.T., Ahmed, H. et al. KSTAR: An algorithm to predict patient-specific kinase activities from phosphoproteomic data. Nat Commun 13, 4283 (2022). https://doi.org/10.1038/s41467-022-32017-5 This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

Recommended Citation

Crowl, Sam; Jordan, Ben T; Ahmed, Hamza; Ma, Cynthia X; and Naegle, Kristen M, "KSTAR: An algorithm to predict patient-specific kinase activities from phosphoproteomic data." Nature Communications. 13, 1. 4283 (2022).
https://digitalcommons.wustl.edu/oa_4/261