Skip to main content

Table 2 Binding Affinity Prediction Performances of different network architectures

From: In silico design of MHC class I high binding affinity peptides through motifs activation map

Model

SRCC

AUC

2CNN+FC

0.178

0.56

2CNN + GAP

0.083

0.554

1CNN + GAP

0.119

0.575

2CNN + muti-GAP

0.117

0.576

3CNN + GAP

0.139

0.59

  1. The training dataset is HLA-A*0201 while the test dataset is IEDB 1029824 HLA-A*0201 segmented from HLA-A*0201. FC denotes full-connected layer. SRCC stands for Spearman’s rank correlation coefficient and AUC stands for area under the receiver operating characteristic curve. All the models are well-trained. “A CNN+B GAP” represents A CNN layers and B Global Pooling Layers in the feature caught part. The “2CNN + muti-GAP” is our final MHC-CNN predictor