Pieter Meysman

The paper applies T-cell receptor sequencing and epitope annotation tools to quality control and immune monitoring of TIL products by identifying tumor-specific clones and screening for off-cancer reactivity.

The paper introduces a computational framework to efficiently identify significantly neighbor enriched TCR sequences, demonstrating its utility in analyzing memory T cells and T cell responses to vaccination and viral infections.

This study analyzes T-cell receptor repertoires in monozygotic twins with and without Crohn's disease, finding increased TCR overlap in concordant pairs and identifying eight novel TCR clusters potentially linked to the disease.

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

This study tracks CD4+ T cell and IgG responses over time after yellow fever vaccination, showing peak activation at day 22 and long-term antibody persistence.

CARTiBASE curates and standardizes over 10,000 CAR sequences into a single resource, enabling interactive browsing, similarity search, and analysis of CAR design trends.

This study compares T cell receptor repertoire diversity and epigenetic clocks to assess healthy aging and CMV impact, finding higher TCR diversity in healthy elderly and CMV-related epigenetic aging.

The paper introduces AIRRWAS, a framework to identify T cell receptor convergence with bacterial genera, revealing reproducible associations across cohorts and functional T cell responses.

This paper introduces LIgO, a software suite that simulates adaptive immune receptor repertoires with immune information to aid in developing and benchmarking AIRR machine learning methods.

The paper creates a synovial fluid TCR, cytokine, and cell repertoire atlas to compare disease mechanisms across arthritic conditions using systems immunology approaches.

The paper links immunotypes and enterotypes through a systems immunology approach, showing they are orthogonal and connected via the blood transcriptome, with associations between specific bacteria and immune health.

A TCR-first approach to single-cell data analysis improves identification of disease-specific TCR features and reveals previously unknown T-cell clusters and arrangements.

This study analyzes T cell receptor repertoires in MS patients and controls to explore the relationship between EBV-specific and myelin-specific immune responses, finding distinct patterns associated with MS.

The paper analyzes the sequence distribution of epitope-specific TCR repertoires and shows how this affects machine learning model performance.

The paper develops computational models to identify WT1-specific TCRs in AML patients and finds higher TCR diversity in remission compared to relapsed patients.

The paper introduces a cross-attention transformer encoder for multi-sequence inputs and evaluates it on peptide-MHCII binding prediction.

The paper shows that biased negative data in TCR-epitope binding prediction leads to poor performance in realistic scenarios.

RapTCR provides a tool for rapid visualization and analysis of T-cell receptor repertoires, capturing both global and local sequence similarities.

This study uses multi-view modelling to improve prediction of HBV vaccine response by integrating bulk RNA-seq, T-cell data, flow cytometry, and clinical metadata.

This study uses interpretable deep learning to analyze TCR-epitope interactions and identify molecular binding patterns that influence TCR affinity.

The paper uses TCR-epitope recognition models to analyze CD8+ T-cell responses in COVID-19 patients, finding that non-critical patients develop unique SARS-CoV-2-specific T-cell responses not seen in critical patients.

The paper benchmarks 23 TCR-epitope prediction models, finding that using paired-chain alpha-beta and CDR1/2/V/J data improves performance, and simple distance-based methods can be competitive.

This study uses multi-omics analysis to show that aneuploidy in Leishmania causes proportional changes in transcript and protein abundance, with post-transcriptional regulation compensating for some effects.

This paper introduces a hierarchical graph convolutional network for predicting compound-protein interactions using molecular structure and protein sequences, showing improved performance over existing methods.

clusTCR is a Python tool for fast clustering of large CDR3 sequences, offering improved speed over existing methods while maintaining comparable accuracy.

This paper evaluates and adapts the CONSTANd normalization method for use with count data from sequencing, showing its efficiency and ability to remove bias across different experimental conditions.

The paper identifies challenges in predicting TCR-epitope interactions and introduces ImRex, a feature representation method that improves model performance for similar epitopes.

The paper shows Leishmania donovani lacks detectable DNA methylation, enabling enrichment of parasite DNA from mixed samples using methyl-binding columns.

MESSAR recommends metabolite substructures from tandem mass spectra without requiring predefined metabolite candidates.

TCRex identifies epitope-specific T cell receptor sequences in repertoires using machine learning and statistical analysis, validated on vaccine and infectious disease datasets.