ArticleFrontiers in aging neuroscience2022
Application of weighted co-expression network analysis and machine learning to identify the pathological mechanism of 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 6 papers.
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
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- Identification of UBE2N as a biomarker of Alzheimer's disease by combining WGCNA with machine learning algorithms.Scientific reports · 2025Article
- Nrf2 activation rescues stress-induced depression-like behaviour and inflammatory responses in male but not female rats.Biology of sex differences · 2024Article
- Intracerebral Hemorrhage-Induced Brain Injury: the Role of Lysosomal-Associated Transmembrane Protein 5.Molecular neurobiology · 2023Article
- Diagnostic models and predictive drugs associated with cuproptosis hub genes in Alzheimer's disease.Frontiers in neurology · 2022Article
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aberrant deposits of neurofibrillary tangles (NFT), the main characteristic of Alzheimer's disease (AD), are highly related to cognitive impairment. However, the pathological mechanism of NFT formation is still unclear. This study explored differences in gene expression patterns in multiple brain regions [entorhinal, temporal, and frontal cortex (EC, TC, FC)] with distinct Braak stages (0- VI), and identified the hub genes
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