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Table 4 Performance comparison with the state-of-the-art methods on three tasks of Dataset2

From: MDDI-SCL: predicting multi-type drug-drug interactions via supervised contrastive learning

 

ACC

AUPR

AUC

F1

Precision

Recall

Task1

 MDDI-SCL

0.9516

0.9862

0.9995

0.9321

0.9162

0.9500

 MDF-SA-DDI

0.9291

0.9773

0.9996

0.9117

0.9381

0.8910

 DDIMDL

0.9229

0.9637

0.9993

0.9105

0.9212

0.9039

 Lee et al.'s methods

0.9370

0.9791

0.9991

0.9181

0.9226

0.9153

 DeepDDI

0.7211

0.7724

0.9914

0.6854

0.6654

0.7183

 DNN

0.7908

0.8539

0.9949

0.7649

0.7560

0.8046

 RF

0.6956

0.7567

0.9892

0.5760

0.6694

0.5426

 KNN

0.5797

0.5964

0.8998

0.3805

0.4758

0.3347

 LR

0.5229

0.5288

0.9805

0.2373

0.3128

0.2185

Task2

 MDDI-SCL

0.6595

0.6794

0.9757

0.5578

0.5605

0.5712

 MDF-SA-DDI

0.6664

0.6820

0.9862

0.5919

0.6526

0.5518

 DDIMDL

0.6720

0.7086

0.9885

0.5817

0.6680

0.5295

 Lee et al.'s methods

0.6917

0.7119

0.9687

0.5934

0.6144

0.5848

 DeepDDI

0.5883

0.5851

0.9746

0.4709

0.5250

0.4361

 DNN

0.6687

0.6838

0.9818

0.6164

0.7279

0.5479

Task3

 MDDI-SCL

0.4696

0.4261

0.9315

0.2838

0.3160

0.2773

 MDF-SA-DDI

0.4794

0.4450

0.9686

0.2937

0.3667

0.2659

 DDIMDL

0.4699

0.4386

0.9685

0.3032

0.3773

0.2729

 Lee et al.'s methods

0.4867

0.4349

0.9093

0.3082

0.3355

0.3066

 DeepDDI

0.3611

0.2820

0.9264

0.1868

0.2301

0.1711

 DNN

0.4570

0.4129

0.9565

0.2997

0.4345

0.2508