Transfer Learning for T-Cell Response Prediction

Josua Stadelmaier, Brandon Malone, Ralf Eggeling
3/18/2024
q-bio.CBcs.LG

No official code found. Best implementation: JosuaStadelmaier/T-cell-response-prediction (1 star, last commit 2024-09-07).

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 is competitive with existing state-of-the-art approaches for predicting T-cell responses for human peptides.

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Code Implementations(1)

JosuaStadelmaier/T-cell-response-predictionCommunity100%

Transfer Learning for T-Cell Response Prediction

10Python, Jupyter NotebookMar 14, 20242 years ago

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Cite this paper

@article{stadelmaier2024transfer,
  title  = {Transfer Learning for T-Cell Response Prediction},
  author = {Josua Stadelmaier and Brandon Malone and Ralf Eggeling},
  year   = {2024},
  eprint = {2403.12117},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2403.12117v2}
}

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