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A multi-platform approach to identify a blood-based host protein signature for distinguishing between bacterial and viral infections in febrile children (PERFORM): a multi-cohort machine learning study

Lookup NU author(s): Professor Marieke Emonts-le ClercqORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

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Author(s): Zandstra J, Jackson HR, Menikou S, et al, Emonts M, PERFORM Consortium

Publication type: Article

Publication status: Published

Journal: Lancet Digital Health

Year: 2023

Volume: 5

Issue: 11

Pages: e774–785

Print publication date: 01/11/2023

Online publication date: 25/10/2023

Acceptance date: 26/07/2023

Date deposited: 31/07/2023

ISSN (electronic): 2589-7500

Publisher: The Lancet Publishing Group

URL: https://doi.org/10.1016/S2589-7500(23)00149-8

DOI: 10.1016/S2589-7500(23)00149-8

Data Access Statement: Normalised proteomic data used in the high-throughput screening phase have been deposited on Zenodo with the following links: SomaScan https://doi.org/10.5281/zenodo.7781290; MS-A https://doi.org/10.5281/zenodo.7801523; MS-B https://doi.org/10.5281/zenodo.7801541. De-identified participant data are provided, including disease group, age (months), and sex. Computational code for all analyses have been uploaded to GitHub in the following repository: https://github.com/PIDBG/PERFORM_proteomics.

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