Transfer Learning for T-Cell Response Prediction
Josua Stadelmaier Brandon Malone Ralf Eggeling
We study the prediction of T-cell response for specific given peptides, which
could, among other applications, be a crucial step towards the development of
personalized cancer vaccines. It is a challenging task due to limited,
heterogeneous training data featuring a multi-domain structure; such data
entail the danger of shortcut learning, where models learn general
characteristics of peptide sources, such as the source organism, rather than
specific peptide characteristics associated with T-cell response.
  Using a transformer model for T-cell response prediction, we show that the
danger of inflated predictive performance is not merely theoretical but occurs
in practice. Consequently, we propose a domain-aware evaluation scheme. We then
study different transfer learning techniques to deal with the multi-domain
structure and shortcut learning. We demonstrate a per-source fine tuning
approach to be effective across a wide range of peptide sources and further
show that our final model outperforms existing state-of-the-art approaches for
predicting T-cell responses for human peptides.