Jason Greenbaum

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.

This study identifies a ubiquitous neoantigen-reactive T-cell response across multiple metastatic sites in a patient with pancreatic neuroendocrine tumor, supporting the use of mutation-sharing status for neoantigen selection in immunotherapy.

EPITOME is a multimodal pipeline that combines OCR, text matching, and VLMs to automate and enhance epitope data curation from scientific papers.

This study identifies an enriched NK-like cytotoxic Vδ3 T cell subset in TB patients using single-cell RNA sequencing, showing persistent transcriptomic differences post-treatment.

The paper identifies that CDR1 and CDR2 sequence identities are more predictive of epitope similarity than CDR3, and develops BCRMatch to predict epitope binding based on antibody sequences.

PDI-TCR identifies Mtb-specific TCRs and characterizes their phenotypes in TB patients and healthy individuals, revealing distinct dynamics of specific and unspecific T cell responses.

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 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.

This paper presents a method to predict epitope activity of non-peptidic compounds using machine learning and the IEDB database, achieving ROC-AUC values between 0.69-0.96.

This paper improves T cell epitope predictions by combining MHC binding affinities with antigen expression levels, showing better performance than methods using only binding or expression data.