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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