Fig. 1.

Supervised and unsupervised learning in $T$ cell epitope specificity inference. (A) SPMs (left) fit a predictive function f(x) to training data having an independent variable X (TCR sequences and other features) and dependent variable y (epitopes or pMHC complexes). This function may then be applied to predict the cognate epitopes of orphan TCRs. UCMs (right) generate a mapping from TCR sequences to a cluster allocation, such that each TCR is assigned to one or more clusters having common epitope specificity. (B) Application of UCMs to de-orphanise TCRs by co-clustering.