Recent advances in machine learning and experimental biology have offered breakthrough
solutions to problems such as protein structure prediction that were long thought to be …

A fundamental prerequisite for the efficacy of cancer immunotherapy is the presence of
functional, antigen-specific T cells within the tumor. Neoantigen-directed therapy is a …

Prediction of T-cell receptor (TCR) interactions with MHC-peptide complexes remains highly
challenging. This challenge is primarily due to three dominant factors: data accuracy, data …

Deep learning algorithms have been utilized to achieve enhanced performance in pattern-
recognition tasks. The ability to learn complex patterns in data has tremendous implications …

Introduction Predicting the binding specificity of T Cell Receptors (TCR) to MHC-peptide
complexes (pMHCs) is essential for the development of repertoire-based biomarkers. This …

The accurate prediction of binding between T cell receptors (TCR) and their cognate
epitopes is key to understanding the adaptive immune response and developing …

Many different solutions to predicting the cognate epitope target of a T-cell receptor (TCR)
have been proposed. However several questions on the advantages and disadvantages of …

Several recent studies investigate TCR-peptide/-pMHC binding prediction using machine
learning or deep learning approaches. Many of these methods achieve impressive results …

The T-cell receptor (TCR) allows T-cells to recognize and respond to antigens presented by
infected and diseased cells. However, due to TCRs' staggering diversity and the complex …

The adaptive immune system stores invaluable information about current and past immune
responses and may serve as an ultrasensitive biosensor. Given the immune system's critical …