Fig. 3From: In silico design of MHC class I high binding affinity peptides through motifs activation mapThe visualization of our low-level Motifs Activation Map network. Take the example of one 9-mer peptide, converting to the feature matrix with the shape of 9 × 16 (16 is the kernel size of the first 1-D convolutional layer) out from the first convolutional layer. The representation of the site, 9, is preserved. Then using the W1 matrix to add each feature from the low-level weight and collect together. Then the feature matrix of size 9 × 1. As the low-level feature with respect to sites, to get the final site rank, we give a weight for low level and then merge all levels feature matrices together. the final result’s shape is still 9*1, we preserve the length through the calculating of sites contribution vector and it provides intuitive information for us to compare the contribution to the binding probability of each siteBack to article page