Jeremy P. Gygi

The paper benchmarks computational models predicting B. pertussis booster vaccine responses using multi-omics data from three cohorts, identifying key predictors of immunity and establishing a community resource for systems vaccinology.

The paper benchmarks 49 algorithms for predicting B. pertussis vaccination outcomes, finding that purpose-built models outperform literature-based ones due to better handling of nonlinearities and data preprocessing.

The paper identifies a multi-omics recovery factor predictive of long COVID using data from the IMPACC study, showing it can predict LC risk early after infection.

This study identifies immune subtypes in hospitalized COVID-19 patients linked to clinical outcomes and long COVID risk through longitudinal multi-omics analysis.

This paper introduces a supervised Bayesian factor model to improve multi-omics signature discovery by integrating dimensionality reduction and predictive modeling.

The paper evaluates computational models predicting immune responses to Pertussis Booster vaccinations using a new community resource, finding that existing models perform poorly and new models show variable performance based on features and strategies.

The paper introduces the Immune Signatures Data Resource, a compendium of standardized systems vaccinology datasets for comparative vaccine response analysis.