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 …

Cancer-specific T cells are required for effective anti-cancer immunity and have a central
role in cancer immunotherapy. However, emerging evidence suggests that only a small …

The identification of the mechanisms by which T-cell receptors (TCRs) interact with human
antigens provides a crucial opportunity to develop new vaccines, diagnostics and …

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 …

The regulatory and effector functions of T cells are initiated by the binding of their cell-
surface T cell receptor (TCR) to peptides presented by major histocompatibility complex …

Accurately predicting peptide secondary structures remains a challenging task due to the
lack of discriminative information in short peptides. In this study, PHAT is proposed, a deep …

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 …

Motivation Predicting the binding between T-cell receptor (TCR) and peptide presented by
human leucocyte antigen molecule is a highly challenging task and a key bottleneck in the …