An Open Benchmark for Systems Vaccinology: Insights from the CMI-PB Challenges
Shinde, P.; Willemsen, L.; Lee, J.; Orfield, S.; Ren, Z.; Aoki, M.; Thrupp, N.; Gupta, A.; Wu, C.-C.; Mao, L.; Li, C.; Tan, Y.; Nguyen, T. A.; Chang, N.-S.; Schafer, P. S. L.; Xing, J.; Can Ali Marandi, C.; Sabuwala, B.; Reyna, J.; Gygi, J. P.; Ha, B.; Overton, J. A.; Einav, T.; Greenbaum, J. A.; Guan, L.; Kojima, M.; Ay, F.; Grant, B.; Kleinstein, S. H.; Peters, B.
Show abstract
Systems vaccinology approaches have identified factors affecting vaccine responses in multiple studies, but the ability of computational models to generalize these findings to unseen data remains unclear. We established a community resource to create and compare models predicting B. pertussis booster vaccination responses and put such modeling approaches to the test. We compiled multi-modal experimental training data from three independent cohorts (n=117 individuals), and asked investigators to predict vaccine responses in a cohort of 54 newly recruited individuals using only their pre-booster vaccination data. We benchmarked a total of 107 computational models. Top-performing models were characterized by workflows that prioritized rigorous data preprocessing, robust imputation of missing data, and the use of multi-omics integration or non-linear machine learning. We identified pre-existing antigen-specific antibody titers and baseline monocyte frequencies as the most consistent predictors of post-vaccination immunity, highlighting the dominant role of individual immune setpoints. We established the resulting datasets and evaluation framework as a community resource to advance predictive immunology and facilitate personalized vaccination strategies.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Immunologic and Biophysical Features of the BNT162b2 JN.1- and KP.2-Adapted COVID-19 Vaccines 92%
- Characterizing SARS-CoV-2 neutralization profiles after bivalent boosting using antigenic cartography 92%
- Deep learning predictions of TCR-epitope interactions reveal epitope-specific chains in dual alpha T cells 92%
Similar papers in this journal
- CAPYBARA: A Generalizable Framework for Predicting Serological Measurements Across Human Cohorts 93%
- Supervised fine-tuning of pre-trained antibody language models improves antigen specificity prediction 93%
- Paraplume: A fast and accurate paratope prediction method provides insights into repertoire-scale binding dynamics 92%
Similar papers in this journal
- Identification of B cell subsets based on antigen receptor sequences using deep learning 92%
- A comprehensive atlas of immunological differences between humans, mice and non-human primates 91%
- Pandemic, epidemic, endemic: B cell repertoire analysis reveals unique anti-viral responses to SARS-CoV-2, Ebola and Respiratory Syncytial Virus 91%
Similar papers in this journal
- Human immunoglobulin gene allelic variation impacts germline-targeting vaccine priming 92%
- Immunofocusing on the conserved fusion peptide of HIV envelope glycoprotein in rhesus macaques 92%
- A novel chimeric coronavirus spike vaccine combining SARS-CoV-2 RBD and scaffold domains from HKU-1 elicits potent neutralising antibody responses 91%
Similar papers in this journal
- Markov Field network integration of multi-modal data predicts effects of immune system perturbations on intravenous BCG vaccination in macaques 95%
- Robust computational design and evaluation of peptide vaccines for cellular immunity with application to SARS-CoV-2 93%
- The mutational landscape of SARS-CoV-2 variants diversifies T cell targets in an HLA supertype-dependent manner 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.