ArticleJournal of immunology (Baltimore, Md. : 1950)2019
SIMON, an Automated Machine Learning System, Reveals Immune Signatures of Influenza Vaccine Responses.
Article in Journal of immunology (Baltimore, Md. : 1950), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Gene expression profiling reveals insights into infant immunological and febrile responses to group B meningococcal vaccine.Molecular systems biology · 2020Trial
- Cellular immunology data enable clinical severity prediction via supervised machine learning.iScience · 2026Article
- Large language models for bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- Integrative mapping of preexisting influenza immune landscapes predicts vaccine response.The Journal of clinical investigation · 2025Article
- Pre-Vaccination Immunotypes and Immune Entropy Are Indicators of Multiple Vaccine Responsiveness.Aging cell · 2025Article
- AI Methods Tailored to Influenza, RSV, HIV, and SARS-CoV-2: A Focused Review.Pathogens (Basel, Switzerland) · 2025Review
- A systems biology approach to define SARS-CoV-2 correlates of protection.NPJ vaccines · 2025Article
- AI in optimized cancer treatment: laying the groundwork for interdisciplinary progress.Oxford open immunology · 2025Article
- Comprehensive analysis of multi-omics vaccine response data using MOFA and Stabl algorithms.Frontiers in bioinformatics · 2025Article
- Application of machine learning algorithms to identify serological predictors of COVID-19 severity and outcomes.Communications medicine · 2024Article
- Conventional and unconventional T-cell responses contribute to the prediction of clinical outcome and causative bacterial pathogen in sepsis patients.Clinical and experimental immunology · 2024Article
- Pre-vaccination immunotypes reveal weak and robust antibody responders to influenza vaccination.Aging cell · 2024Article
- Personalized cancer vaccine design using AI-powered technologies.Frontiers in immunology · 2024Review
- Application of machine learning models to identify serological predictors of COVID-19 severity and outcomes.Research square · 2023Article
- Supervised Machine Learning Methods for Seasonal Influenza Diagnosis.Diagnostics (Basel, Switzerland) · 2023Article
- A Machine Learning-Based Analytic Pipeline Applied to Clinical and Serum IgG Immunoproteome Data To Predict Chlamydia trachomatis Genital Tract Ascension and Incident Infection in Women.Microbiology spectrum · 2023Article
- Analyses of human vaccine-specific circulating and bone marrow-resident B cell populations reveal benefit of delayed vaccine booster dosing with blood-stage malaria antigens.Frontiers in immunology · 2023Article
- FastMix: a versatile data integration pipeline for cell type-specific biomarker inference.Bioinformatics (Oxford, England) · 2022Article
- Signaling differences in peripheral blood mononuclear cells of high and low vaccine responders prior to, and following, vaccination in piglets.Vaccine: X · 2022Article
- A Framework for Augmented Intelligence in Allergy and Immunology Practice and Research-A Work Group Report of the AAAAI Health Informatics, Technology, and Education Committee.The journal of allergy and clinical immunology. In practice · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Machine learning holds considerable promise for understanding complex biological processes such as vaccine responses. Capturing interindividual variability is essential to increase the statistical power necessary for building more accurate predictive models. However, available approaches have difficulty coping with incomplete datasets which is often the case when combining studies. Additionally, there are hundreds of algorithms available and no simple way to find the optimal one. In this study, we developed Sequential Iterative Modeling "OverNight" (SIMON), an automated machine learning system that compares results from 128 different algorithms and is particularly suitable for datasets containing many missing values. We applied SIMON to data from five clinical studies of seasonal influenza vaccination. The results reveal previously unrecognized CD4
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Registered trials
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