Fig. 2: The small-sample learning framework and performance of KprFunc.
From: Small-sample learning reveals propionylation in determining global protein homeostasis
a The architecture of KprFunc, which incorporates PWD, SMO, PLR, DNN and MAML algorithms. b ROC curves and AUC values of tenfold cross-validations of KprFunc-i and other methods. c ROC curves and AUC values demonstrating the performance of KprFunc-i and KprFunc in distinguishing functional Kpr sites from other sites. d The confusion matrix of KprFunc-i under the medium threshold (Sp ≥ 90%). e The confusion matrix of KprFunc under the medium threshold (Sp ≥ 90%). f GO-based enrichment analysis of circadian proteins with functional Kpr sites predicted by KprFunc. E-ratio, enrichment ratio (one-sided hypergeometric test).
