Fig. 1From: Semi-supervised learning for somatic variant calling and peptide identification in personalized cancer immunotherapySchematic representation of supervised classification (a) versus PLATO’s PU learning approach (b). Supervised classification requires training data and can perform poorly when the distributions of training and test data do not match. PU learning uses an existing model-based classifier with stringent thresholds and informed undersampling to train a classifier from the data itselfBack to article page