Journal article
The FluPRINT dataset, a multidimensional analysis of the influenza vaccine imprint on the immune system
- Abstract:
- Machine learning has the potential to identify novel biological factors underlying successful antibody responses to influenza vaccines. The first attempts have revealed a high level of complexity in establishing influenza immunity, and many different cellular and molecular components are involved. Of note is that the previously identified correlates of protection fail to account for the majority of individual responses across different age groups and influenza seasons. Challenges remain from the small sample sizes in most studies and from often limited data sets, such as transcriptomic data. Here we report the creation of a unified database, FluPRINT, to enable large-scale studies exploring the cellular and molecular underpinnings of successful antibody responses to influenza vaccines. Over 3,000 parameters were considered, including serological responses to influenza strains, serum cytokines, cell phenotypes, and cytokine stimulations. FluPRINT, facilitates the application of machine learning algorithms for data mining. The data are publicly available and represent a resource to uncover new markers and mechanisms that are important for influenza vaccine immunogenicity.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 1.7MB, Terms of use)
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- Publisher copy:
- 10.1038/s41597-019-0213-4
Authors
+ EU Framework Programme for Research and Innovation H2020
More from this funder
- Grant:
- FluPRINT, No 796636
- Publisher:
- Springer Nature
- Journal:
- Scientific Data More from this journal
- Volume:
- 6
- Article number:
- 214
- Publication date:
- 2019-10-21
- Acceptance date:
- 2019-08-27
- DOI:
- EISSN:
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2052-4463
- Pmid:
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31636302
- Language:
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English
- Keywords:
- Pubs id:
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pubs:1065319
- UUID:
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uuid:7648b1f9-a3db-4d68-95f0-77f18baeebf3
- Local pid:
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pubs:1065319
- Source identifiers:
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1065319
- Deposit date:
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2019-12-11
- ARK identifier:
Terms of use
- Copyright holder:
- Tomic, A
- Copyright date:
- 2019
- Notes:
-
© The Author(s) 2019. Open Access. 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.
The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article.
- Licence:
- CC Attribution (CC BY)
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