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Structure of the proposed GCNN model. The model includes two parts: graph convolution and a fully connected output layer for classification. Input is 1D gene expression levels of TCGA samples and the adjacency matrix of genes ( input graph). The graph is then pooled into a single GCNN layer to be fed into the hidden and output layers.
