Skip to main content

Table 3 Prediction accuracies obtained on twenty two breeds of goat

From: Development of a model webserver for breed identification using microsatellite DNA marker

Breed

Bayes network

Sensitivity

Specificity

Accuracy (*)

MCC

FDR

Blackbengal

0.958

0.998

0.996 (0.005)

0.956

0.042

Ganjam

0.958

0.997

0.995 (0.005)

0.946

0.061

Gohilwari

0.958

0.998

0.996 (0.005)

0.956

0.042

Jharkhandblack

0.833

0.994

0.986 (0.006)

0.844

0.130

Attapaddy

0.854

0.997

0.990 (0.006)

0.887

0.068

Changthangi

0.979

0.998

0.997 (0.003)

0.968

0.041

Kutchi

0.978

0.999

0.998 (0.005)

0.977

0.022

Mehsana

0.854

0.996

0.989 (0.006)

0.877

0.089

Sirohi

1.000

1.000

1.000 (0.000)

1.000

0.000

Malabari

0.917

0.993

0.989 (0.006)

0.884

0.137

Jamunapari

0.458

0.980

0.956 (0.003)

0.467

0.476

Jhakarana

0.625

0.990

0.973 (0.011)

0.671

0.250

Surti

0.750

0.995

0.984 (0.008)

0.803

0.122

Gaddi

0.917

0.986

0.983 (0.005)

0.825

0.241

Marwari

0.667

0.976

0.961 (0.021)

0.597

0.429

Barbari

0.729

0.995

0.983 (0.009)

0.790

0.125

Beetal

0.792

0.991

0.982 (0.007)

0.790

0.191

Kanniadu

0.979

0.986

0.986 (0.011)

0.862

0.230

Sangamnari

0.938

0.999

0.996 (0.002)

0.956

0.022

Osmanabadi

0.979

0.996

0.995 (0.006)

0.947

0.078

Zalawari

1.000

1.000

1.000 (0.000)

1.000

0.000

Cheghu

0.791

0.989

0.981 (0.017)

0.763

0.244

Weighted Avg.

0.858

0.993

0.987

0.851

0.142

  1. *The values in parenthesis are the respective standard deviations computer from 5-fold cross validation.
  2. Data in bold represent the weighted average, where weights are the sample sizes for each breed.
Back to article page

BMC Genomic Data

ISSN: 2730-6844

Contact us