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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