Self Supervised Prediction of Genetic Associations in Comorbid Diseases With Masked Autoencoder Using Hypergraph...

Comorbid disease association refers to the simultaneous occurrence of a disease with the coexistence of another primary disease. Due to the complex traits of these co-occurring multi-diseases, it is crucial to know the underlying genetic molecular basis of the prevalent diseases. The inference of common genetic association based on gene co-expression data helps to unveil the pathogenesis of comorbid diseases. There exist a few disease-specific gene co-expression-based analyses to predict the hub genes causing these diseases. However, works lack multi-relational biological data integration. In addition, there still does not exist any unified method to predict the common genetic associations from the co-expression graph across comorbid diseases. Hence, we introduce a generalized and novel approach to predict overlapping genetic associations from disease-specific gene co-expression networks with a self-supervised edge-masking technique catapult with a hypergraph-based pre-embedding learning approach. The advantage of hypergraph learning is that it induces higher-order rich biological information of candidate genes. In addition, we use the self-supervised-based edge masking strategy to attain model training over only a few numbers of edge labels. Our proposed approach outperforms the six baseline models for our case-study datasets and also predicts novel genetic associations across comorbid disease pairs.