Bjoern Peters

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.

The paper describes how the OBO Foundry formalized its principles into operational rules and implemented automated checks to evaluate ontology compliance, improving interoperability and quality.

The paper compares immune responses to a Tdap booster in adults primed with acellular (aP) or whole-cell (wP) pertussis vaccines, identifying divergent gene expression and antibody subtype patterns in aP-primed individuals.

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.

The paper improves MHC II epitope prediction by integrating mass spectrometry data with a new machine learning framework, achieving better performance than existing methods.

This study benchmarks T cell epitope prediction tools using a comprehensive dataset of vaccinia virus epitopes, finding that neural network-based methods trained on MHC binding and ligand elution data perform best.

The paper develops a web tool to quantify and visualize gene signature expression in blood cell types using RNA data.

MOGSA integrates multiple omics data to improve gene set analysis by combining features across data types and reducing noise, revealing molecular subtypes in bladder cancer.

The paper identifies T cell:monocyte complexes in human blood as biological immune interactions, not technical artifacts, and shows their frequency changes with immune perturbations.

A sequence-only convolutional network trained on T cell receptor (TCR)-peptide pairs for HLA-A*02:01 can single out TCRs that bind a target peptide among many non-binding ones.

This study introduces two algorithms for inferring gene-level cell-to-cell communications between immune cell types and evaluates their performance using a membrane protein database.

NetMHCpan-4.0 improves peptide-MHC class I interaction predictions by integrating binding affinity and eluted ligand data, enhancing identification of naturally processed ligands and T cell epitopes.

DAFi identifies cell populations from flow cytometry data by combining recursive filtering and clustering with user-defined constraints, improving resolution of overlapping and rare populations.