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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