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Table 4 Sensitivities and specificities of the SNP classifier combinations

From: A Bayesian method for comparing and combining binary classifiers in the absence of a gold standard

∼ 1000 positives

Sensitivity

Specificity

2K-bit code

Combination

centerMean

Median

SD

Mean

Median

SD

2

C1∧C2∧C3

0.083

0.082

0.017

1.000

1.000

0.0000

4

C2∧C3

0.098

0.097

0.018

1.000

1.000

0.0000

6

C1∧C3

0.144

0.143

0.020

1.000

1.000

0.0000

18

C1∧C2

0.483

0.481

0.053

1.000

1.000

0.0000

24

At least two classifiers

0.559

0.558

0.053

1.000

1.000

0.0000

64

C2∨C3

0.645

0.647

0.047

0.997

0.997

0.0001

96

C1∨C3

0.869

0.869

0.035

0.997

0.997

0.0001

120

C1∨C2

0.932

0.933

0.021

0.998

0.998

0.0001

128‡

C1∨C2∨C3

0.943

0.944

0.018

0.996

0.996

0.0002

∼ 5000 positives

 

Sensitivity

Specificity

2K-bit code

Combination

Mean

Median

SD

Mean

Median

SD

2

C1∧C2∧C3

0.065

0.066

0.007

1.000

1.000

0.0000

4

C2∧C3

0.079

0.079

0.007

1.000

1.000

0.0000

6

C1∧C3

0.123

0.123

0.008

1.000

1.000

0.0000

18

C1∧C2

0.439

0.440

0.025

1.000

1.000

0.0000

24

At least two classifiers

0.510

0.511

0.026

1.000

1.000

0.0000

64

C2∨C3

0.601

0.601

0.024

0.987

0.987

0.0002

96

C1∨C3

0.850

0.852

0.018

0.991

0.991

0.0004

120

C1∨C2

0.918

0.919

0.011

0.994

0.994

0.0004

128‡

C1∨C2∨C3

0.930

0.931

0.010

0.986

0.986

0.0004

  1. SD: Standard Deviation.
  2. ‡Optimal combination using ranking criteria 1, 2, 3 and 4.