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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