geneDRAGNN: Gene Disease Prioritization using Graph Neural Networks

Many human diseases exhibit a complex genetic etiology impacted by various genes and proteins in a large network of interactions. The process of evaluating gene-disease associations through in-vivo experiments is both time-consuming and expensive. Thus, network-based computational methods capable of modeling the complex interplay between molecular components can lead to more targeted evaluation. In this paper, we propose and evaluate geneDRAGNN: a general data processing and machine learning methodology for exploiting information about gene-gene interaction networks for predicting gene-disease association. We demonstrate that information derived from the gene-gene interaction network can significantly improve the performance of gene-disease association prediction models. We apply this methodology to lung adenocarcinoma, a histological subtype of lung cancer. We identify new potential gene-disease associations and provide supportive evidence for the association through gene-set enrichment and literature based analysis.