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Table 1 Comparison of K-fold cross-validation performance for 4 GDSC drug sensitivity prediction approaches – Latent Regression Prediction (LRP), Latent-Latent Prediction (LLP), Combined Latent Prediction (CLP) and Direct Prediction (DP), using data from CCLE

From: Application of transfer learning for cancer drug sensitivity prediction

Drug Pearson Correlation NRMSE
  LRP LLP CLP DP LRP LLP CLP DP
17-AAG 0.5441 0.4691 0.6382 0.4591 0.2117 0.2147 0.1930 0.2164
AZD6244 0.3988 0.4155 0.4524 0.4008 0.1833 0.1718 0.1684 0.1703
Nilotinib 0.9053 0.3886 0.8768 0.4524 0.0728 0.1295 0.0888 0.1242
Nutlin-3 0.4093 0.5473 0.5646 0.5108 0.1965 0.1756 0.1745 0.1799
PD-0325901 0.6448 0.4502 0.6606 0.4465 0.1614 0.1870 0.1585 0.1878
PD-0332991 0.2497 0.0912 0.2540 0.0884 0.1695 0.1729 0.1672 0.1733
PLX4720 0.5682 0.5040 0.6384 0.5001 0.1237 0.1290 0.1173 0.1291
  1. Bold values indicate the best performance