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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).


Publication metadata

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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Funding

Funder referenceFunder name
Consorcio Centro de Investigación Biomédica en Red de Enfermedades Respiratorias
European Union Horizon 2020
European Union Seventh Framework Programme (EUCLIDS)
Imperial Biomedical Research Centre
Instituto de Salud Carlos III
Grupos de Refeencia Competitiva
Medical Research Foundation
National Institute for Health Research
Swiss State Secretariat for Education, Research and Innovation
Wellcome Trust

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