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Found 3 papers, 2 papers with code
What cleaves? Is proteasomal cleavage prediction reaching a ceiling?
While direct identification of proteasomal cleavage \emph{in vitro} is cumbersome and low throughput, it is possible to implicitly infer cleavage events from the termini of MHC-presented epitopes, which can be detected in large amounts thanks to recent advances in high-throughput MHC ligandomics.
Improved proteasomal cleavage prediction with positive-unlabeled learning
Accurate in silico modeling of the antigen processing pathway is crucial to enable personalized epitope vaccine design for cancer.
Uncertainty-aware Pseudo-label Selection for Positive-Unlabeled Learning
In this work, we thus propose to tackle the issues of imbalanced datasets and model calibration in a PUL setting through an uncertainty-aware pseudo-labeling procedure (PUUPL): by boosting the signal from the minority class, pseudo-labeling expands the labeled dataset with new samples from the unlabeled set, while explicit uncertainty quantification prevents the emergence of harmful confirmation bias leading to increased predictive performance.
