Multi-level attention graph neural network based on co-expression gene modules for disease diagnosis and prognosis

Motivation Advanced deep learning techniques have been widely applied in disease diagnosis and prognosis with clinical omics, especially gene expression data. In the regulation of biological processes and disease progression, genes often work interactively rather than individually. Therefore, investigating gene association information and co-functional gene modules can facilitate disease state prediction. Results To explore the gene modules and inter-gene relational information contained in the omics data, we propose a novel multi-level attention graph neural network (MLA-GNN) for disease diagnosis and prognosis. Specifically, we format omics data into co-expression graphs via weighted correlation network analysis, and then construct multi-level graph features, finally fuse them through a well-designed multi-level graph feature fully fusion module to conduct predictions. For model interpretation, a novel full-gradient graph saliency mechanism is developed to identify the disease-relevant genes. MLA-GNN achieves state-of-the-art performance on transcriptomic data from TCGA-LGG/TCGA-GBM and proteomic data from coronavirus disease 2019 (COVID-19)/non-COVID-19 patient sera. More importantly, the relevant genes selected by our model are interpretable and are consistent with the clinical understanding. Availabilityand implementation The codes are available at .

Automated summary

This study proposes a novel multi-level attention graph neural network (MLA-GNN) for disease diagnosis and prognosis, which investigates gene association information and co-functional gene modules to facilitate disease state prediction. The experimental results demonstrate that MLA-GNN achieves state-of-the-art performance on transcriptomic and proteomic data, and the selected relevant genes are consistent with clinical understanding.