Benson Ogunjimi

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.

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.

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

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.

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

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

The paper evaluates unsupervised clustering methods for T-cell receptor sequences and finds distinct clusters that target the same epitopes.