ArticleScientific data2019
The FluPRINT dataset, a multidimensional analysis of the influenza vaccine imprint on the immune system.
Article in Scientific data, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Literature review: Current trends and future prospects of digital vaccine supply chain support technology.Human vaccines & immunotherapeutics · 2025Pooled it
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- CITMIC: Comprehensive Estimation of Cell Infiltration in Tumor Microenvironment based on Individualized Intercellular Crosstalk.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Comprehensive analysis of multi-omics vaccine response data using MOFA and Stabl algorithms.Frontiers in bioinformatics · 2025Article
- Multisite development and validation of machine learning models to predict severe outcomes and guide decision-making for emergency department patients with influenza.Journal of the American College of Emergency Physicians open · 2024Article
- Identification of Pathogenic Immune Cell Subsets Associated With Checkpoint Inhibitor-Induced Myocarditis.Circulation · 2022Article
- Mucosal vaccines - fortifying the frontiers.Nature reviews. Immunology · 2022Review
- Divergent trajectories of antiviral memory after SARS-CoV-2 infection.Nature communications · 2022Article
- treekoR: identifying cellular-to-phenotype associations by elucidating hierarchical relationships in high-dimensional cytometry data.Genome biology · 2021Article
- Article
- Review
- A robust and interpretable end-to-end deep learning model for cytometry data.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.