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Viral Infections and Outbreaks Research / Vaccines and Immunoinformatics Approaches · Journal article
The Journal of Immunology · July 28, 2026
Raises a question worth testing. It does not answer one.
This is an exploratory machine-learning analysis of a multi-vaccine transcriptomic database that identifies interferon-signaling and plasmablast pathways as correlates of post-vaccination antibody responses. The analysis demonstrates that gradient-boosting and neural network models can capture immune kinetics explaining ~15% of antibody response variance, but lacks independent validation, prospective confirmation, or demonstration of clinical predictive utility for vaccine development.
Retrospective observational analysis of transcriptomic and immunological data using machine learning. 1405 healthy adults aged 18 years or older; no specific exclusion criteria reported. Vaccines spanned live (yellow fever, smallpox), recombinant viral-vector (Ebola), inactivated (influenza), and glycoconjugate (pneumococcal) categories.. Intervention: Analysis of post-vaccination transcriptomic and immunological responses; no intervention administered.. n = 1,405.
Post-vaccination time points explained approximately 15% of total variance in antibody responses across vaccine types Age and sex contributed minimally to antibody response prediction Gradient-boosting and multi-output modeling approaches achieved highest predictive accuracy across vaccines
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These findings suggest that transcriptomic signatures, particularly interferon-signaling and plasmablast pathways, may be useful biomarkers for vaccine durability; however, external validation and prospective clinical application remain necessary before integration into vaccine development workflows.
This is a post-hoc exploratory machine-learning analysis of observational transcriptomic and immunological data identifying correlates of antibody durability across vaccines, without prospective validation or clinical outcome prediction in a held-out cohort.
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These findings suggest that transcriptomic signatures, particularly interferon-signaling and plasmablast pathways, may be useful biomarkers for vaccine durability; however, external validation and prospective clinical application remain necessary before integration into vaccine development workflows.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
Abstract Introduction Understanding vaccine durability is key to designing immunizations with long-term efficacy. Leveraging the Immune Signatures Data Resource, a compendium of transcriptomic and immunological responses from 1405 healthy adults (18+ years) across 24 vaccines, we investigated shared immune mechanisms underlying durable antibody responses. The dataset spans live (yellow fever, smallpox), recombinant viral-vector (Ebola), inactivated (influenza), and glycoconjugate (pneumococcal) vaccines. Methods Data preprocessing included imputation, normalization, and alignment across post-vaccination time points. We applied advanced machine learning (ML) frameworks to predict antibody immunogenicity and durability. Feature selection for high-dimensional, low-sample-size multi-omics datasets was performed using HSIC Lasso to identify predictors of antibody responses. Selected features served as input to ensemble, regularized regression, and gradient-boosting models (e.g. DT, RF, LASSO, XGB, CatBoost). We compared single-target and multi-output approaches, evaluating stacked, chained, and wrapper-based strategies, and implemented multi-layer neural networks to capture complex relationships among immune features. Results Post-vaccination time points explained ∼15% of the total variance, indicating shared immune kinetics across vaccine types, while age and sex contributed minimally. Gradient-boosting and multi-output modeling approaches achieved the highest predictive accuracy across vaccines, highlighting the value of integrating correlated outcomes. Neural network models similarly captured complex, nonlinear immune signatures, albeit with reduced explainability. Conclusion Conserved transcriptional modules, particularly interferon-signaling and plasmablast-related pathways, emerged as strong predictors of antibody durability. This integrative ML framework enables identification of key immune signatures critical for developing vaccines with durable responses, advancing data-driven strategies for systems vaccinology. Funding Source n/a Topic Categories Computational and Systems Immunology (COMP)
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