Skip to main content

Table 1 Performance of NEMII based on different feature combinations

From: A network embedding-based multiple information integration method for the MiRNA-disease association prediction

 

AUPR

AUC

F1

ACC

REC

SPEC

PRE

combination 1

0.6036 ± 0.0018

0.9252 ± 0.0014

0.6072 ± 0.0020

0.9955 ± 0.0001

0.4860 ± 0.0052

0.9992 ± 0.0001

0.8128 ± 0.0158

combination 2

0.2630 ± 0.0032

0.7890 ± 0.0056

0.3338 ± 0.0032

0.9933 ± 0.0000

0.2360 ± 0.0025

0.9987 ± 0.0000

0.5681 ± 0.0058

combination 3

0.6086 ± 0.0015

0.9284 ± 0.0012

0.6129 ± 0.0031

0.9956 ± 0.0001

0.4887 ± 0.0069

0.9992 ± 0.0001

0.8247 ± 0.0160

combination 4

0.6085 ± 0.0024

0.9262 ± 0.0018

0.6115 ± 0.0026

0.9956 ± 0.0000

0.4836 ± 0.0055

0.9993 ± 0.0001

0.8366 ± 0.0105

combination 5

0.6104 ± 0.0012

0.9293 ± 0.0017

0.6147 ± 0.0025

0.9956 ± 0.0001

0.4893 ± 0.0060

0.9993 ± 0.0001

0.8289 ± 0.0164

  1. * combination 1: SDNE feature alone
  2. * combination 2: miRNA-family feature and disease similarity feature
  3. * combination 3: SDNE feature and miRNA-family feature
  4. * combination 4: SDNE feature and disease similarity feature
  5. * combination 5: SDNE feature, miRNA-family feature and disease similarity feature
Back to article page

BMC Bioinformatics

ISSN: 1471-2105

Contact us