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Table 6 The average test performance of the SSL-GCN models with different similarity levels of unlabeled subsets (close, normal, far) on the 12 prediction tasks in 5 repeated experiments

From: Chemical toxicity prediction based on semi-supervised learning and graph convolutional neural network

Tasks

SSL-GCN (all)

SSL-GCN (close)

SSL-GCN (normal)

SSL-GCN (far)

AUC

Std

AUC

Std

AUC

Std

AUC

Std

NR-AR-LBD

0.7647

0.0279

0.7353

0.0353

0.7410

0.0210

0.7726

0.0242

NR-AR

0.7967

0.0251

0.7398

0.0594

0.7389

0.0401

0.7351

0.0357

NR-AhR

0.8303

0.0055

0.8261

0.0076

0.8292

0.0080

0.8278

0.0055

NR-Aromatase

0.7337

0.0057

0.7318

0.0082

0.7222

0.0131

0.7382

0.0145

NR-ER-LBD

0.6870

0.0282

0.6731

0.0261

0.6532

0.0207

0.6609

0.0253

NR-ER

0.7188

0.0196

0.7214

0.0087

0.7108

0.0133

0.7190

0.0107

NR-PPAR-gamma

0.7614

0.0212

0.7435

0.0493

0.7538

0.0164

0.7493

0.0171

SR-ARE

0.7297

0.0080

0.7308

0.0066

0.7099

0.0118

0.7172

0.0081

SR-ATAD5

0.7175

0.0143

0.6896

0.0261

0.6855

0.0295

0.7095

0.0113

SR-HSE

0.7822

0.0097

0.7833

0.0116

0.7700

0.0071

0.7745

0.0075

SR-MMP

0.8120

0.0075

0.8096

0.0097

0.8099

0.0091

0.8080

0.0091

SR-p53

0.7518

0.0198

0.7159

0.0208

0.7417

0.0129

0.7279

0.0113

  1. For comparison, the best results of the SSL-GCN models trained with the entire unlabeled dataset (all) are shown. The complete test performance can be found in the Additional file 1
  2. The bold number denotes the best result among all models (all, close, normal, far) in the corresponding task, the underlined number represents only the best result among models using different similarity levels of unlabeled subsets (close, normal, far)