Transfer Learning for T-Cell Response Prediction
<|reference_start|>Transfer Learning for T-Cell Response Prediction: 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.<|reference_end|>
@article{stadelmaier2024transfer,
    title={Transfer Learning for T-Cell Response Prediction},
    author={Josua Stadelmaier (University of T"ubingen), Brandon Malone (NEC
  OncoImmunity), Ralf Eggeling (University of T"ubingen)},
    journal={arXiv preprint arXiv:2403.12117},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.12117},
    primaryClass={q-bio.CB cs.LG}
}