Table 1 The predictive performance of our method in a series of cross-validation experiments.

From: Large-scale prediction of microRNA-disease associations by combinatorial prioritization algorithm

Random MicroRNAs

Leave-One-Out

50a

100a

150a

200a

250a

300a

All microRNAs1

84.12%c

85.00%c

85.61%c

86.10%c

86.56%c

86.76%c

86.93%

Ab Initio

50a

100a

150a

200a

250a

300a

All microRNAs1

69.23%c

68.89%c

69.68%c

70.00%c

70.44%c

71.30%c

70.92%

Random Diseases

Leave-One-Out

90b

100b

110b

120b

130b

140b

All diseases2

81.53%c

80.66%c

79.44%c

83.43%c

81.52%c

83.47%c

83.50%

Ab Initio

90b

100b

110b

120b

130b

140b

All diseases2

77.97%c

77.28%c

79.38%c

79.27%c

79.63%c

79.02%c

80.03%

  1. 1The candidate microRNAs were obtained after deleting the microRNA (microRNAs) related to query disease.
  2. 2The candidate diseases were obtained after deleting the disease (diseases) associated with the query microRNA.
  3. aThe number of randomly selected candidate microRNAs in microRNA prioritization.
  4. bThe number of randomly selected candidate diseases in disease prioritization.
  5. cThe AUC score of ROC curve.