المفاهيم الأساسية
Transfer learning techniques improve T-cell response prediction for peptide vaccines by addressing shortcut learning and negative transfer between domains.
الملخص
The content discusses the use of transfer learning in predicting T-cell responses for peptide vaccines, focusing on personalized cancer vaccines. It addresses challenges such as limited data, shortcut learning, and data heterogeneity from different sources. The study proposes a domain-aware evaluation scheme and explores transfer learning techniques like per-source fine-tuning to enhance model performance. Results show that the proposed approach outperforms existing state-of-the-art methods for predicting T-cell responses.
Introduction
Discusses the role of T cells in recognizing cancerous or infected cells.
Highlights the importance of predicting T-cell response for developing peptide-based vaccines.
Materials and Methods
Describes the construction of a T-cell response dataset using the Immune Epitope Database.
Introduces the transformer model architecture for capturing T-cell response patterns in peptides.
Results
Investigates shortcut learning in models trained on multi-domain data sets.
Explores negative transfer between domains and evaluates per-source fine-tuning approach.
Model Evaluation
Measures model performance using AUC based on ground-truth labels and predictions.
Conducts validation studies to compare different models' performance on human peptides.
Comparison to Other Tools
Compares FINE-T model with existing pre-trained models like NetMHCpan 4.1, PRIME 2.0, and CD4Episcore for predicting T-cell responses in human peptides.
الإحصائيات
Using a transformer model improves predictive performance practically.
Source code available at github.com/JosuaStadelmaier/T-cell-response-prediction.
اقتباسات
"The danger of inflated predictive performance is not merely theoretical but occurs in practice."
"Per-source fine-tuning is effective in enabling positive transfer across a wide range of peptide sources."