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Table 3 Performances of miPred and miR-KDE for the NH3350 dataset in terms of genus.

From: Using a kernel density estimation based classifier to predict species-specific microRNA precursors

  %SE %SP %ACC %Fm %MCC
Vertebrata
   miPred 95.3% 88.8% 92.1% 92.3% 84.3%
   miR-KDE 93.4% 92.8% 93.1% 93.2% 86.3%
with miPred's %SP 96.1% 88.8% 92.5% 92.7% 85.2%
Arthropoda
   miPred 98.8% 89.0% 93.9% 94.2% 88.2%
   miR-KDE 100.0% 92.0% 96.0% 96.2% 92.3%
Viridiplantae
   miPred 98.2% 93.6% 95.9% 96.0% 91.9%
   miR-KDE 98.4% 95.0% 96.7% 96.8% 93.4%
Nematoda
   miPred 97.2% 90.4% 93.8% 94.0% 87.8%
   miR-KDE 97.2% 92.7% 94.9% 95.0% 89.9%
Viruses
   miPred 97.2% 93.1% 95.1% 95.2% 90.4%
   miR-KDE 94.4% 97.2% 95.8% 95.8% 91.7%
with miPred's %SP 98.6% 93.1% 95.8% 95.9% 91.8%
Overall
   miPred 97.3% ± 1.3% 91.0% ± 2.3% 94.1% ± 1.5% 94.3% ± 1.4% 88.5% ± 2.9%
   miR-KDE 96.7% ± 2.7% 93.9% ± 2.1% 95.3% ± 1.4% 95.4% ± 1.4% 90.7% ± 2.8%
with miPred's %SP 98.1% ± 1.5% 92.3% ± 2.2% 95.2% ± 1.6% 95.3% ± 1.6% 90.5% ± 3.3%
  1. The best performance among each dataset is highlighted in bold.