Before revision:  Results: We investigated whether there is a relationship between interaction networks and gene expression values by using a graph convolutional auto-encoder and two end-to-end learning approaches for three interaction networks and hundreds of experimental conditions in the URLanismE.\ coli . Graph neural networks use a message passing framework to learn an embedding of a graph in a continuous space , either using network topology alone, or including additional node features.
After revision:  Results: We investigated whether there is a relationship between interaction networks and gene expression values by using a graph convolutional auto-encoder and two end-to-end learning approaches for three interaction networks and hundreds of experimental conditions in the URLanismSimultaneously, omics technologies measure the variation or activity of genes, proteins, or metabolites across individuals or experimental conditions. Integrating the complementary viewpoints of biological networks and omics data is an important task in bioinformatics, but existing methods treat networks as discrete structures, which are intrinsically difficult to integrate with continuous node features or activity measures . Graph neural networks use a message passing framework to learn an embedding of a graph in a continuous space , either using network topology alone, or including additional node features.