The pitfalls of negative data bias for the T-cell epitope specificity challenge

Summary / Abstract Even high-performing machine learning models can have problems when deployed in a real-world setting if the data used to train and test the model contains biases. TCR–epitope binding prediction for novel epitopes is a very important but yet unsolved problem in immunology. In this article, we describe how the technique used to create negative data for the TCR–epitope interaction prediction task can lead to a strong bias and makes that the performance drops to random when tested in a more realistic scenario.