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Table 6 Experimental results of performance comparison on two benchmark datasets

From: RLFDDA: a meta-path based graph representation learning model for drug–disease association prediction

Dataset

Methods

AUC.

Prec.

Recall

F1-score

B-dataset

deepDR

0.8205

0.8813

0.2345

0.3704

DTINet

0.8323

0.9712

0.1781

0.3009

GIPAE

0.8602

0.7869

0.7768

0.7788

HINGRL

0.8696

0.7875

0.7953

0.7913

RLFDDA

0.8728

0.7821

0.8060

0.7938

C-dataset

deepDR

0.9028

0.9894

0.5450

0.7021

DTINet

0.8741

0.9974

0.1376

0.2409

GIPAE

0.9026

0.7619

0.9202

0.8336

HINGRL

0.9592

0.9058

0.8878

0.8966

RLFDDA

0.9636

0.9035

0.8972

0.9002

  1. Best results are bolded
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BMC Bioinformatics

ISSN: 1471-2105

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