Identification of Potential Driver Genes and Pathways Based on Transcriptomics Data in Alzheimer's Disease.

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. To identify AD-related genes from transcriptomics and help to develop new drugs to treat AD. In this study, firstly, we obtained differentially expressed genes (DEG)-enriched coexpression networks between AD and normal samples in multiple transcriptomics datasets by weighted gene co-expression network analysis (WGCNA). Then, a convergent genomic approach (CFG) integrating multiple AD-related evidence was used to prioritize potential genes from DEG-enriched modules. Subsequently, we identified candidate genes in the potential genes list. Lastly, we combined deepDTnet and SAveRUNNER to predict interaction among candidate genes, drug and AD. Experiments on five datasets show that the CFG score of is the highest among all potential driver genes of AD. Moreover, we found interacts with AD from target-drugs-diseases network prediction. Therefore, candidate gene is the most likely to be target of AD. In summary, identification of AD-related genes contributes to the understanding of AD pathophysiology and the development of new drugs.