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Table 9 Performance of the H1N1 models on H3N2 data and vice versa

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

  Protein Sensitivity Specificity MCC
H3N2 data - H1N1 models HA 1 0 na
M1 1 0.895 0.941
M2 1 0.73 0.84
NA 1 0 na
NP 1 0.882 0.932
NS1 1 0.747 0.85
NEP 1 0.648 0.78
PA-X 0 1 na
PA 0.021 0.93 −0.11
PB1-F2 0.023 1 0.056
PB1 0.563 0.909 0.302
PB2 0.979 0.949 0.873
H1N1 data - H3N2 models HA 0 na na
M1 0.957 0.975 0.885
M2 0.987 0.766 0.804
NA 1 0 −0.004
NP 0.364 0.984 0.251
NS1 0.365 0.993 0.237
NEP 0.027 1 0.061
PA-X 0.201 0.982 0.223
PA 0.247 0.995 0.177
PB1-F2 0.991 0.804 0.832
PB1 0.992 0.877 0.888
PB2 0.956 0.951 0.786
  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