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Table 2 Comparison of K-fold cross-validation performance for three GDSC drug sensitivity prediction approaches – Mapped Prediction (MP), CCLE model Prediction (CP) and Direct Prediction (DP) using data from CCLE

From: Application of transfer learning for cancer drug sensitivity prediction

Drug Pearson Correlation NRMSE
  MP CP DP MP CP DP
17-AAG 0.6062 0.4354 0.4591 0.2112 0.3073 0.2164
AZD6244 0.4692 0.3580 0.3579 0.1683 0.2173 0.1743
Nilotinib 0.8698 0.7957 0.4524 0.1093 0.1323 0.1242
Nutlin-3 0.5606 0.3102 0.5114 0.1852 0.2180 0.1808
PD-0325901 0.6132 0.5731 0.4224 0.1689 0.1875 0.1865
PD-0332991 0.0923 0.0305 0.0802 0.1748 0.1764 0.1755
PLX4720 0.6335 0.6135 0.5001 0.1242 0.159 0.1291
  1. Bold values indicate the best performance