Bjoern Peters

This paper identifies 23 candidate causal genes for critical COVID-19 by integrating severity-stratified, longitudinal cis-eQTL mapping with GWAS signals from 1,031 patients.

The paper uses image-guided sorting to distinguish physically interacting T cell-monocyte pairs from coincidental doublets in tuberculosis patients, revealing distinct transcriptional signatures of contact.

Image-based gating on an imaging spectral cytometer resolves 12 high-purity immune doublet types in human blood; in tuberculosis, doublet frequencies are unchanged but tightly contacting T cell-monocyte doublets increase.

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 benchmarks generative models for adaptive immune receptor repertoires, identifying their strengths and limitations in capturing biological features.

The paper revises the standardization and validation pipeline for immune receptor data in the IEDB to improve data consistency and usability.

The paper introduces the Vaccine Ontology (VO), a standardized framework for representing vaccines and related data, enhancing data integration and AI research.

NetMHCIIphosPan predicts HLA class II antigen presentation of phosphorylated peptides using immunopeptidomics data, outperforming existing tools.

IMMREP25 evaluates TCR:pMHC binding prediction for unseen peptides, showing improved performance with structural modeling but still below random guessing.

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

This study examines immune cell differences in close contacts of TB patients based on QFT status, finding minimal differences among subgroups but clear immune changes in active TB patients.

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.

This study characterizes the phenotypic and functional diversity of neoantigen-specific CD4+ T cell responses to a growing tumor and shows how immunotherapy alters their composition and efficacy.

The paper introduces a method to study cell-cell interactions at single-cell resolution, revealing unique transcriptomic signatures in T cell-monocyte complexes during TB and dengue infection.

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.

This study compares immune responses to whole-cell and acellular pertussis vaccines, finding that Th1 polarization is maintained through an early interferon response in whole-cell vaccine recipients.

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.

The paper analyzes T cell epitopes from H5N1 and seasonal influenza viruses, finding conserved epitopes and cross-reactivity, suggesting pre-existing immunity may reduce H5N1 severity.

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

This study investigates viral reactivation in COVID-19 patients, finding associations between reactivation of specific virus families and clinical outcomes, including mortality and PASC symptoms.

This paper develops a machine learning model that integrates peptide and antigen features to predict immunogenic CD4+ T cell epitopes in bacteria, achieving high accuracy across multiple bacterial species.

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 improves HLA-DQ antigen presentation prediction using machine learning and shows trans-only variants have limited impact on the immunopeptidome.

This study evaluates preprocessing methods and sample size requirements for gene network inference using RNA-seq data, finding that no single method is optimal and recommending dataset-specific evaluations.

BepiPred-3.0 improves B-cell epitope prediction using protein language models, achieving high accuracy for both linear and conformational epitopes.

The paper curates a collection of >600 immune signatures from human vaccination studies and provides a data model and web dashboard for querying these signatures.

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

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.