Tricked by Edge Cases: Can Current Approaches Lead to Accurate Prediction of T-Cell Specificity with Machine Learning?
This paper argues that current machine learning approaches for predicting T-cell receptor (TCR) specificity are limited by flawed equilibrium-based training data and proposes a new framework that integrates cell-based kinetic measurements with machine learning to achieve more accurate and mechanistically grounded predictions.