Table 5

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

Method

DRB1*0101

DRB1*0301

DRB1*0401

DRB1*0701

DRB1*1101

DRB1*1501

Ave


IEDB_SMM

0.81

0.71

0.79

0.67

0.84

0.67

0.75

IEDB_SAT

0.89

0.69

0.75

0.74

0.83

0.66

0.76

IEDB_Cons

0.83

0.67

0.72

0.80

0.84

0.66

0.75

Multipred_SVM

0.86

    0.80

0.65

0.70

0.78

0.62

0.74

NetMHCII

0.77

0.69

0.81

0.62

0.78

0.65

0.72

NetMHCIIpan

0.84

0.65

0.81

    0.83

0.81

0.79

0.79

Propred

0.89

0.70

0.75

0.74

0.83

0.66

0.76

SVMHC

0.86

0.69

0.75

0.74

0.83

0.66

0.76

SYF

0.72

0.65

0.69

0.70

0.59

0.77

0.69


NN-W-P1

    0.90

0.78

    0.84

0.75

    0.85

0.79

0.82

NN-W

    0.90

0.79

0.80

0.76

0.83

    0.82

0.81


NN-W-P1 is the NN-based method including data redundancy step-size rescaling and P1-PSSM encoding and NN-W the NN-based method including data redundancy step-size rescaling. All other methods are described in the Lin et al. publication. The AUC was calculated using the following binding affinity threshold values for each of the 6 alleles: DRB1*0101, 0401, 0701, and 1501 threshold = 100 nM, DRB1*0301, 1101, and 1301, threshold = 1000 nM (Lin HH personal communication). The performance values for the 9 methods above the dotted line were taken from Lin et al. [24]. For each allele, the best performing NN method is highlighted in bold and the best performing method is underlined.

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

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