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Table 3 Results for models trained with human data

From: A Support Vector Machine based method to distinguish long non-coding RNAs from protein coding transcripts

Test data set    
Method GRCh37 GRCh38 NONCODE
Radial using GRCh37 and first ORF
  Sensitivity 9 8 . 9 5 % 9 9 . 4 3 % 96.67%
  Specificity 97.41% 97.23% -
  Accuracy 9 8 . 1 8 % 98.33% -
Radial using GRCh38 and first ORF
  Sensitivity 89.86% 97.54% 88.75%
  Specificity 9 8 . 6 4 % 9 9 . 2 6 % -
  Accuracy 94.25% 9 8 . 4 0 % -
CPCa,e
  Sensitivity 67.23% 69.90% -
  Specificity 97.62% 73.90% -
  Accuracy 82.43% 71.90% -
CPATa,e
  Sensitivity 94.60% 89.90% -
  Specificity 85.28% 92.40% -
  Accuracy 89.94% 91.20% -
lncRScan-SVMa
  Sensitivity 93.88% - -
  Specificity 89.20% - -
  Accuracy 91.94% - -
iSeeRNAb,c
  Sensitivity 96.10% - -
  Specificity 94.70% - -
  Accuracy 95.40% - -
lncRNApredd,f
  Sensitivity - - 93.40%
  Specificity - - -
  Accuracy - - -
FEELnce
  Sensitivity - 92.30% -
  Specificity - 91.50% -
  Accuracy - 91.90% -
  1. Results in bold are the best for each test data set. Note that our method produced the best results
  2. aResults obtained in Han et al. [25]
  3. bResults obtained in Sun et al. [27]
  4. cThis method was created to classify only lincRNAs
  5. dResults obtained in Sun et al. [24]
  6. eResults obtained in Wucher et al. [28]
  7. fWe only considered sensitivity, since the negative test data was not clearly specified in the article