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Table 4 Comparison of pan-specific HLA-DR prediction results on the dataset proposed by the authors of NetMHCIIpan

From: Learning a peptide-protein binding affinity predictor with kernel ridge regression

 

PCC

RMSE (kcal/mol)

 

MHC β chain

KRR+GS

MultiRTA

NetMHCIIpan-2.0

KRR+GS

MultiRTA

# of examples

DRB1*0101

0.662

0.619

0.627

1.48

1.33

5166

DRB1*0301

0.743

0.438

0.560

1.29

1.36

1020

DRB1*0401

0.667

0.534

0.652

1.36

1.56

1024

DRB1*0404

0.709

0.623

0.731

1.18

1.33

663

DRB1*0405

0.606

0.566

0.626

1.25

1.28

630

DRB1*0701

0.694

0.620

0.753

1.34

1.51

853

DRB1*0802

0.728

0.523

0.700

1.23

1.45

420

DRB1*0901

0.471

0.375

0.474

1.53

2.01

530

DRB1*1101

0.786

0.603

0.721

1.16

1.46

950

DRB1*1302

0.416

0.365

0.337

1.73

1.68

498

DRB1*1501

0.612

0.513

0.598

1.46

1.57

934

DRB3*0101

0.654

0.603

0.474

1.52

1.10

549

DRB4*0101

0.540

0.508

0.515

1.41

1.61

446

DRB5*0101

0.732

0.543

0.722

1.28

1.60

924

Average:

0.644

0.531

0.606

1.37

1.49

 
  1. Best results for each metric are highlighted in bold. The PCC results show that the proposed method (KRR+GS) outperforms MultiRTA with a p-value of 0.001 and NetMHCIIpan-2.0 with a p-value of 0.0574. The RMSE results indicate that KRR+GS outperforms MultiRTA with a p-value of 0.0466.
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BMC Bioinformatics

ISSN: 1471-2105

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