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Table 2 Performance comparison between our method and the three state-of-the-art prediction methods

From: Cross disease analysis of co-functional microRNA pairs on a reconstructed network of disease-gene-microRNA tripartite

Methods

sample size

cv type

Specificity

Sensitivity

Accuracy

AUC

RLSMDA

1184+

LOOCV

–

–

–

0.9475

our model

1184+,1184-

LOOCV

0.9367

0.9368

0.9367

0.9896

Xu’s method

37+, 44-

5-fold

0.8833

0.8643

0.8772

0.9189

our model

37+, 37-

5-fold

0.9990

1.000

0.9995

0.9854

Jiang’s method

270+, 270-

10-fold

0.9125

0.7338

0.8232

0.8884

our model

263+, 263-

10-fold

0.9274

0.8982

0.9128

0.9871

  1. Symbols “+/-” represent “positive samples/negative samples”. cv means cross-validation
  2. The best performance among the compared methods are showed in boldface
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BMC Bioinformatics

ISSN: 1471-2105

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