Fig. 1
From: Predicting anticancer hyperfoods with graph convolutional networks
Drug targets are represented as a binary signal on the PPI. We use a GNN to generate a graph embedding representing the systemic-wide effect of the drug on the PPI. We then feed this representation to an MLP for the anticancer prediction task. The model is trained in an end-to-end fashion. After model training, we feed bioactive molecules within foods to the model for the prediction of anticancer food molecules