This project focuses on analyzing and predicting Gene-Disease Associations (GDA) using graph-based machine learning techniques. It leverages curated datasets, protein-protein interaction (PPI) data, and various node features to construct graph representations of gene-disease associations. Two graph neural network architectures, GraphSAGE and GAT.
