Table 4

Predictive performance in terms of the AUC on the Wang benchmark data set.

Allele

ARB

MHC2Pred

MHCpred

Propred

Rankpep

SMM-align

SVRMHC

SYF

Cons

NN-W-P1

NN-W


DRB1*0101

0.76

0.67

0.62

0.74

0.70

0.77

0.69

0.71

0.79

    0.88

0.87

DRB1*0301

0.66

0.53

0.65

0.67

0.69

0.50

0.72

    0.82

    0.82

DRB1*0401

0.67

0.52

0.60

0.69

0.63

0.68

0.66

0.65

0.69

    0.73

0.72

DRB1*0404

0.72

0.64

0.79

0.66

0.75

0.80

    0.83

    0.83

DRB1*0405

0.67

0.51

0.75

0.62

0.69

0.62

0.72

    0.81

0.80

DRB1*0701

0.69

0.63

0.78

0.58

0.78

0.68

0.83

0.86

    0.87

DRB1*0802

0.74

0.70

0.77

0.75

    0.82

0.79

0.81

DRB1*0901

0.62

0.48

0.61

0.66

0.73

0.68

0.68

    0.69

DRB1*1101

0.73

0.60

0.80

0.70

0.81

0.80

    0.89

    0.89

DRB1*1302

0.79

0.54

0.58

0.52

0.69

0.73

    0.78

    0.78

DRB1*1501

0.70

0.63

0.72

0.62

0.74

0.64

0.67

0.72

    0.77

0.76

DRB3*0101

0.59

0.68

0.85

    0.86

DRB4*0101

0.74

0.61

0.65

0.71

0.74

    0.86

    0.86

DRB5*0101

0.70

0.59

0.79

0.73

0.75

0.63

0.79

    0.87

    0.87


Ave

0.70

0.59

0.62

0.73

0.64

0.73

0.65

0.66

0.76

0.82

0.82


NN-W-P1 is the NN-based method including data redundancy step-size rescaling and P1-PSSM encoding and NN-W is the NN-based method including data redundancy step-size rescaling. Both methods were evaluated using 10-fold cross-validation. The performance values for the 9 other methods were taken from Wang et al. [18]. For each allele, the best performing NN method is highlighted in bold and the best performing of all methods is underlined.

Nielsen and Lund BMC Bioinformatics 2009 10:296   doi:10.1186/1471-2105-10-296

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