From: In silico design of MHC class I high binding affinity peptides through motifs activation map
Type | Notes |
---|---|
Input layer | Â |
Embedding(each site vec dim = 15) | Finally build N*15 matrix(N is the mer number in peptide, N is 9 denotes 9 mer) |
Conv1D[filter_size=16, filter_length=7] + LeakyReLU(0.3) | Low-level feature |
Dropout(0.25) | Â |
Conv1D[filter_size=32, filter_length=7] + LeakyReLU(0.3) | High-level feature |
Dense layer1(1) without bias | Global averaging Pooling network, input is the first Conv1D |
Dense layer2(1) without bias | Global averaging Pooling network, input is the second Conv1D |
Dense layer3(1) without bias | Fusion of the different level GAP layers(aka voting method) |
Sigmoid [prediction] | Â |