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Table 4 SNF-NN performance comparison with baseline machine learning methods on the LRSSL benchmark dataset

From: SNF-NN: computational method to predict drug-disease interactions using similarity network fusion and neural networks

Method

Accuracy

Specificity

Precision

Recall

F1-score

MCC

AUC-ROC

AUC-PR

SAMME

0.665

0.705

0.680

0.624

0.650

0.331

0.665

0.746

DT

0.635

0.775

0.689

0.494

0.574

0.282

0.635

0.718

GPC

0.701

0.717

0.709

0.685

0.696

0.403

0.701

0.776

KNN

0.661

0.553

0.633

0.769

0.694

0.329

0.661

0.759

GNB

0.616

0.509

0.596

0.723

0.653

0.238

0.616

0.729

QDA

0.567

0.662

0.613

0.473

0.453

0.161

0.567

0.675

RF

0.611

0.683

0.631

0.539

0.580

0.225

0.611

0.700

Linear-SVM

0.678

0.672

0.676

0.685

0.680

0.357

0.678

0.759

RBF-SVM

0.578

0.869

0.687

0.286

0.403

0.191

0.578

0.665

SNF-NN

0.846

0.780

0.821

0.793

0.807

0.617

0.936

0.903

  1. The best value of each evaluation metric is shown in bold