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Table 1 Results on lincRNA Acceptor Data

From: A deep learning method for lincRNA detection using auto-encoder algorithm

a

I

II

III

IV

V

TP

49.4

49.4

49.0

49.4

49.4

FP

0.0

0.2

0.0

1.4

50.6

FN

0.0

0.4

0.0

0.1

0.0

TN

50.5

50.4

0.6

49.2

0.0

b

I

II

III

IV

V

Sn

100.0

99.2

100.0

99.9

100.0*

Sp

99.9

99.6

100.0

97.2

0.0

Acc

100.0

99.4

100.0

98.5

49.4

Mcc

99.9

98.8

100.0

97.1

–

Ppv

99.9

99.6

100.0

97.2

49.4

Pc

99.9

98.8

100.0

97.1

49.4

F1

100.0

99.4

100.0

98.5

66.1

  1. I: DAX, II: EIIP, III: Complimentary, IV: Enthalpy, V: Galois
  2. Panel a: the measurement of methods
  3. TP: True positive
  4. FP: False positive
  5. FN: False negative
  6. TN: True negative
  7. Panel b: the evaluation of methods
  8. Sensitivity, Sn=TP/(TP+FN)
  9. Specificity, Sp=TN/(TN+FP)
  10. Accuracy, Acc=(TP+TN)/(TP+FP+FN+TN)
  11. Matthews correlation coefficient, \(Mcc=TP\times TN - FN\times FP \over {\sqrt {(TP + FN) \times (TN + FP) \times (TP + FP) \times (TN + FN)} }\)
  12. Positive predictive value, Ppv=TP/(TP+FP)
  13. Performance coefficient, Pc=TP/(TP+FN+FP)
  14. F1 score, the harmonic mean of precision and sensitivity, F1=2×TP/(2×TP+FP+FN)
  15. *: Not eligible for comparison due to training failure
  16. –: Invalid value