Skip to main content

Table 3 SNF-NN performance comparison with baseline machine learning methods on the Cdataset 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

0.666

0.683

0.672

0.649

0.660

0.333

0.666

0.748

DT

0.611

0.809

0.711

0.412

0.505

0.253

0.610

0.709

GPC

0.707

0.604

0.692

0.811

0.738

0.433

0.707

0.799

KNN

0.695

0.560

0.654

0.830

0.731

0.406

0.695

0.785

GNB

0.654

0.645

0.651

0.662

0.656

0.308

0.654

0.741

QDA

0.629

0.472

0.599

0.786

0.679

0.272

0.629

0.746

RF

0.618

0.612

0.618

0.624

0.620

0.237

0.618

0.715

Linear-SVM

0.692

0.673

0.685

0.712

0.698

0.386

0.692

0.771

RBF-SVM

0.530

1.000

0.994

0.060

0.112

0.172

0.530

0.762

SNF-NN

0.783

0.754

0.769

0.813

0.790

0.569

0.879

0.856

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