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Table 3 Performance of the models on their corresponding complete data sets

From: Identification of combinatorial host-specific signatures with a potential to affect host adaptation in influenza A H1N1 and H3N2 subtypes

  H1N1 H3N2
Protein Sensitivity Specificity MCC Sensitivity Specificity MCC
HA 0.999 0.953 0.961 1 0.987 0.993
M1 1 0.881 0.934 0.994 1 0.971
M2 1 0.859 0.918 0.996 0.873 0.908
NA 1 0.907 0.95 1 0.908 0.95
NP 1 0.864 0.92 0.994 0.957 0.946
NS1 0.998 0.932 0.954 0.991 0.993 0.96
NEP 0.995 0.883 0.912 0.997 1 0.988
PA-X 0.901 1 0.856 1 1 1
PA 0.972 0.979 0.892 0.996 0.979 0.969
PB1-F2 0.91 0.987 0.884 0.999 0.778 0.861
PB1 0.993 0.93 0.923 1 0.879 0.932
PB2 0.989 0.984 0.935 0.996 0.985 0.972
  1. Sensitivity is the ability to correctly predict human sequences and specificity is the ability to correctly predict avian sequences where 1 means perfect prediction and 0 means no correct prediction. Matthews correlation coefficient (MCC) value is a measure of how well the model performs overall where 1 means a perfect classification, 0 is for a prediction no better than random and −1 indicates a total disagreement between predictions and observations. “na” means the measure could not be calculated for the given model