ArticleFrontiers in aging neuroscience2022
Identification of Potential Driver Genes and Pathways Based on Transcriptomics Data in Alzheimer's Disease.
Article in Frontiers in aging neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 8 citations in OpenAlex.
- Identifying Alzheimer's disease-associated genes using PhenoGeneRanker.bioRxiv : the preprint server for biology · 2024Article
- Identifying patterns to uncover the importance of biological pathways on known drug repurposing scenarios.BMC genomics · 2024Article
- A review and analysis of key biomarkers in Alzheimer's disease.Frontiers in neuroscience · 2024Review
- 1-L Transcription in Alzheimer's Disease.Current issues in molecular biology · 2022Article
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Authors and funding
4 authors at 1 institution in 1 country.
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Abstract
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
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