Transfer Learning for T-Cell Response Prediction

• URL: http://arxiv.org/abs/2403.12117v1
• Date: Mon, 18 Mar 2024 17:32:19 GMT
• Title: Transfer Learning for T-Cell Response Prediction
• Authors: Josua Stadelmaier, Brandon Malone, Ralf Eggeling,
• Abstract summary: We study the prediction of T-cell response for specific given peptides. We show that the danger of inflated predictive performance is not merely theoretical but occurs in practice.
• Score: 0.1874930567916036
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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