Table 2 Comparison with multiple evaluation metrics under fivefold and tenfold cross-validation.

From: Inferring pseudogene–MiRNA associations based on an ensemble learning framework with similarity kernel fusion

 

Model

Precision

Sensitivity

F1-score

Acc

AUC

AUPR

MCC

Fivefold cross-validation

GBDT-LR

0.8200

0.8166

0.8179

0.8176

0.9044

0.9144

0.6358

ABMDA

0.9832

0.2834

0.4381

0.6411

0.9550

0.9519

0.3966

CD_LNLP

0.7780

0.4822

0.5954

0.9876

0.6953

0.5216

0.6069

LAGCN

0.1632

0.8076

0.2712

0.9832

0.9481

0.4847

0.3582

ELPMA

0.9716

0.9369

0.9540

0.9548

0.9897

0.9914

0.9102

Tenfold cross-validation

GBDT-LR

0.8278

0.8306

0.8287

0.8275

0.9078

0.9145

0.6558

ABMDA

0.9848

0.3478

0.5078

0.6728

0.9592

0.9551

0.4487

CD_LNLP

0.8594

0.5605

0.6785

0.9899

0.7854

0.6264

0.6895

LAGCN

0.1007

0.8261

0.1794

0.9853

0.9544

0.4633

0.2852

ELPMA

0.9727

0.9414

0.9565

0.9573

0.9906

0.9922

0.9155

  1. Significant values are in bold.
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