The primary purpose of this project is to identify cancer-related genes using PPI networks and
machine learning and data representation models. We integrated the protein-protein
interaction (PPI) network with gene expression data in this analysis. We obtained useful features
using gene expression data for each gene and then used different feature learning algorithms,
such as DeepWalk, node2vec, and GCN, to find a representation for each node (gene) in the PPI
network. The extracted embedding vectors were used to predict cancer-related genes using
machine learning models, such as SVM and deep neural networks.