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<http://schema.org/caption>
"Figure 1. Convolutional Neural Network Architecture. 1) The input of the model is a list of integer indexes of trigrams generated from protein sequence and vector of size 256 for protein PPI network representation. The trigram indexes are passed to embedding layer which provides vector representations of size 128 for each trigram. The output of an embedding layer is a matrix of size 1000x128 on which we apply convolution and max-pooling. We merge the flattened output of the max-pooling layer with input PPI network representation which is then passed to hierarchical classification layers. 2) Hierarchical classification layers has a DAG structure of GO for is-a relations and for each GO term we created two fully-connected layers. First layer (yellow circle) has 256 neurons and it takes protein representation. Second layer (purple square) has 1 neuron and it is connected to the first layers and performs the classification for one GO term. Additionally, internal nodes in the graph have maximum merge layers (rounded purple square) which outputs the maximum value of all the children and second layer of the term. Finally, the output vector of the model is the concatenation of maximum merge layers of the internal nodes and classification layers of the leaf nodes."
<https://www.semanticscholar.org/paper/DeepGO%3A-predicting-protein-functions-from-sequence-Kulmanov-Khan/48326bdcbb094fce48b17390f7743c23cd0a1ebc>