TMGE: a multi-view graph embedding model for prediction task of genes associated with neurodegenerative diseases

Predicting genes associated with diseases is of great significance for the early diagnosis and efficient treatment of neurodegenerative diseases. However, with the increasing complexity of omics data, it is difficult to integrate many aspects of relationships and effectively mine the genes associated with neurodegenerative diseases. To address this problem, we propose a novel multi-view graph embedding model, named TMGE, which can effectively integrate multiple networks to learn gene representations for prediction task of genes associated with neurodegenerative diseases. First, we design view-specific and view-shared encoders to extract view-specific content and capture consistent information across views; this enables the separation and preservation of diverse information within multi-view data for more accurate representations. Secondly, we introduce cross-linked encoders to enhance the integration of multi-view information, enabling the imputation of missing data across views by capturing cross-view dependencies. Furthermore, we employ adversarial learning to reduce the distribution discrepancy between diverse views at the global level, and then optimize the mean square error of local pairs with the help of the cross-linked encoder structure to achieve local alignment. We conduct experiments to verify the effectiveness of TMGE for predicting genes associated with neurodegenerative diseases using two disease datasets of Alzheimer and Huntington.