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.

我们研究了特定给定肽段的 T 细胞反应的预测，这对于个性化癌症疫苗的开发来说可能是一个关键步骤。我们使用了转换器模型进行 T 细胞反应预测，发现膨胀的预测性能不仅仅是理论上的问题，而是在实践中发生的。因此，我们提出了一个领域感知的评估方案，并研究了不同的迁移学习技术来处理多领域结构和捷径学习。我们证明了一种基于源的细调方法在各种肽源上都有效，并进一步证明我们的最终模型优于现有的最先进方法，用于预测人类肽段的 T 细胞反应。