GNNs and Graph Generative models for biomedical applications

Abstract

Graph generative models are recently gaining significant interest in current application domains. They are commonly used to model social networks, knowledge graphs, and protein-protein interaction networks. In this talk we will present the potential of graph generative models and our recent relevant efforts in the biomedical domain. More specifically we present a novel architecture that generates medical records as graphs with privacy guarantees. We capitalize and modify the graph Variational autoencoders (VAEs) architecture. We train the generative model with the well known MIMIC medical database and achieve generated data that are very similar to the real ones yet provide privacy guarantees. We also develop new GNNs for predicting antibiotic resistance and other protein related downstream tasks such as enzymes classifications and Gene Ontology classification. We achieve there as well promising results with potential for future application in broader biomedical related tasks. Finally we present future research directions for multi modal generative models involving graphs.