CaliPPer introduces a distance-based framework that quantifies generalizability, predicts performance metrics like AUROC with low error, and improves predictions on unseen binding data across multiple models and domains.
A.et al.Deep neural networks predict class i major histocompatibility complex epitope presentation and transfer learn neoepitope immunogenicity.Nat
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CE 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
CaliPPer: quantifying, predicting and improving AI model performance for binding prediction
CaliPPer introduces a distance-based framework that quantifies generalizability, predicts performance metrics like AUROC with low error, and improves predictions on unseen binding data across multiple models and domains.