Fig. 1
From: A multimodal graph neural network framework for cancer molecular subtype classification
The overall structure of the proposed model has four major modules shown as dotted grey rectangles. The input graph consists of inter-omics (red edges), intra-omic (blue edges) edges and miRNA-miRNA meta-path (black dashed edges), and three omics data, mRNA (orange boxes), CNV (yellow boxes), and miRNA (green boxes) is shown as the leftmost side. Module 1 consists of two parallel linear dimension-increase layers for gene-based nodes and miRNA-based nodes. The upgraded graph shown in the middle is obtained by feeding the node attributes from the input graph through module 1, where the dark orange boxes are the updated gene-based node attributes and the dark green boxes are the updated miRNA-based node attributes. Module 2 consists of two graph neural network layers, which can be any graph neural networks. The output of module 2 is then fed into a max pooling layer and then a transformation layer to obtain the learned graph representation (blue boxes). Module 3 consists of a decoder to reconstruct the graph representation back to the input graph node attributes. Module 4 consists of a shallow fully connected network that takes the updated node attributes as the input. The output of the parallel network (grey cubes) is then concatenated with the learned graph representation, and passes through a classification layer for the classification task