A Comprehensive Analysis Identified Hub Genes and Associated Drugs in Alzheimer's Disease.

Alzheimer's disease (AD) is the most common neurodegenerative disease among the elderly and has become a growing global health problem causing great concern. However, the pathogenesis of AD is unclear and no specific therapeutics are available to provide the sustained remission of the disease. In th...

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Publicado en:BioMed Research International pp. 1 - 11
Autores principales: Jing, Qi, Zhang, Hui, Sun, Xiaoru, Xu, Yaru, Cao, Silu, Fang, Yiling, Zhao, Xuan, Li, Cheng
Formato: research tables/charts Journal Article
Publicado: Wiley-Blackwell 1/11/2021
Acceso en línea:Ver este registro en EBSCOhost
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      dt: 1/11/2021
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      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        10.1155/2021/8893553
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        atl: A Comprehensive Analysis Identified Hub Genes and Associated Drugs in Alzheimer's Disease.
      aug:
        au:
          Jing, Qi
          Zhang, Hui
          Sun, Xiaoru
          Xu, Yaru
          Cao, Silu
          Fang, Yiling
          Zhao, Xuan
          Li, Cheng
        affil: Department of Anesthesiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China
      sug:
        subj:
          Bioinformatics
          Gene Expression
          Temporal Lobe
          Cognition
          Alzheimer's Disease Prevention and Control
          Human
          Genes Physiology
          Alzheimer's Disease Therapy
          Prediction Models
          Machine Learning
          Algorithms
          Pharmacogenetics
          Alzheimer's Disease Diagnosis
      ab: Alzheimer's disease (AD) is the most common neurodegenerative disease among the elderly and has become a growing global health problem causing great concern. However, the pathogenesis of AD is unclear and no specific therapeutics are available to provide the sustained remission of the disease. In this study, we used comprehensive bioinformatics to determine 158 potential genes, whose expression levels changed between the entorhinal and temporal lobe cortex samples from cognitively normal individuals and patients with AD. Then, we clustered these genes in the protein-protein interaction analysis and identified six significant genes that had more biological functions. Besides, we conducted a drug-gene interaction analysis of module genes in the drug-gene interaction database and obtained 26 existing drugs that might be applied for the prevention and treatment of AD. In addition, a predictive model was built based on the selected genes using different machine learning algorithms to identify individuals with AD. These findings may provide new insights into AD therapy.
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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