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Table 2 Comparison of GTB with other typical classifiers on heterogenous network-derived features

From: Predicting effective drug combinations using gradient tree boosting based on features extracted from drug-protein heterogeneous network

Method

Precision

Recall

F-Measure

MCC

AUC

GTB

0.897

0.872

0.884

0.772

0.949

kNN

0.738

0.833

0.783

0.542

0.768

SVM

0.882

0.779

0.840

0.728

0.859

Logistic

0.499

0.527

0.510

0.014

0.520

Naive Bayes

0.504

0.988

0.770

0.086

0.508

Random forest

0.880

0.841

0.862

0.733

0.866

AdaBoost

0.878

0.854

0.863

0.732

0.866

LogitBoost

0.803

0.820

0.811

0.617

0.808

  1. The boldface figures indicate that GTB achieves the best performance compared with other typical classifiers on heterogenous network-derived features
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BMC Bioinformatics

ISSN: 1471-2105

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