Sebastian Nymann Deleuran

This paper introduces a supervised learning denoising method to identify low-quality labels in TCR specificity data, improving model reliability by filtering noisy examples.

The paper introduces TCRdenoise, a method to denoise TCR-pMHC interaction data by clustering and similarity metrics, improving the quality of training data for predictive models.

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

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

The paper introduces NetTCR-struc, a GNN-based method to improve TCR-pMHC interaction modeling by enhancing docking quality scoring and structural model selection.

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