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| |
PLS
|
Method 1
|
NetMHCII
|
NetMHCIIPan
|
|---|
| |
AROC
|
r
2
|
AROC
|
r
2
|
AROC
|
r
2
|
AROC
|
r
2
|
|---|
| |
SB
|
WB
| |
SB
|
WB
| |
SB
|
WB
| |
SB
|
WB
| |
|---|
|
DRB1*0101
|
0.713
|
0.579
|
0.541
|
0.838
|
0.645
|
0.796
|
0.848
|
0.691
|
0.811
|
0.835
|
0.647
|
0.753
|
|
DRB1*0301
|
0.675
|
0.610
|
0.476
|
0.987
|
0.954
|
0.996
|
0.958
|
0.882
|
0.966
|
0.841
|
0.602
|
0.736
|
|
DRB1*0401
|
0.690
|
0.537
|
0.491
|
0.986
|
0.956
|
0.995
|
0.951
|
0.845
|
0.945
|
0.778
|
0.631
|
0.636
|
|
DRB1*0404
|
0.695
|
0.559
|
0.595
|
0.986
|
0.961
|
0.995
|
0.940
|
0.845
|
0.954
|
0.854
|
0.630
|
0.769
|
|
DRB1*0405
|
0.702
|
0.577
|
0.527
|
0.985
|
0.966
|
0.996
|
0.927
|
0.846
|
0.947
|
0.809
|
0.588
|
0.682
|
|
DRB1*0701
|
0.729
|
0.612
|
0.559
|
0.987
|
0.958
|
0.997
|
0.965
|
0.893
|
0.963
|
0.879
|
0.716
|
0.801
|
|
DRB1*0802
|
0.776
|
0.602
|
0.587
|
0.990
|
0.980
|
0.997
|
0.979
|
0.880
|
0.973
|
0.841
|
0.550
|
0.770
|
|
DRB1*0901
|
0.659
|
0.532
|
0.403
|
0.988
|
0.961
|
0.997
|
0.969
|
0.899
|
0.956
|
0.813
|
0.576
|
0.673
|
|
DRB1*1101
|
0.681
|
0.565
|
0.550
|
0.981
|
0.957
|
0.996
|
0.968
|
0.893
|
0.969
|
0.855
|
0.594
|
0.787
|
|
DRB1*1302
|
0.600
|
0.521
|
0.441
|
0.978
|
0.830
|
0.997
|
0.981
|
0.837
|
0.965
|
0.806
|
0.579
|
0.759
|
|
DRB1*1501
|
0.656
|
0.552
|
0.494
|
0.987
|
0.960
|
0.995
|
0.940
|
0.795
|
0.945
|
0.768
|
0.544
|
0.667
|
|
DRB3*0101
|
0.595
|
0.510
|
0.451
|
0.983
|
0.932
|
0.996
|
0.956
|
0.872
|
0.935
|
0.879
|
0.613
|
0.737
|
|
DRB4*0101
|
0.724
|
0.667
|
0.604
|
0.987
|
0.966
|
0.997
|
0.686
|
0.942
|
0.976
|
0.892
|
0.621
|
0.795
|
|
DRB5*0101
|
0.727
|
0.607
|
0.553
|
0.985
|
0.958
|
0.997
|
0.960
|
0.884
|
0.965
|
0.872
|
0.649
|
0.789
|
|
Average
|
0.687
|
0.574
|
0.519
|
0.975
|
0.927
|
0.982
|
0.931
|
0.857
|
0.948
|
0.837
|
0.610
|
0.740
|
- The performance of partial least squares (PLS) compared to the neural network regression base on amino acid principal components (NN PCAA) described with two neural network predictors based on substitution matrices. SB and WB columns are the area under the receiver operator curve (AROC) obtained by converting the continuous for the regression fit output to a categorical output SB = strong binder (< 50 nM) WB = weak binder (> 50 nM and <500 nM) and non-binder (> 500 nM). The r2 is indicated is the metric for how well the particular predictor predicts the values in the training set.
