The Construction of Common and Specific Significance Subnetworks of Alzheimer’s Disease from Multiple Brain Regions.

Alzheimer’s disease (AD) is a progressively and fatally neurodegenerative disorder and leads to irreversibly cognitive and memorial damage in different brain regions. The identification and analysis of the dysregulated pathways and subnetworks among affected brain regions will provide deep insights...

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Publicado en:BioMed Research International Vol. 2015; pp. 1 - 14
Autores principales: Kong, Wei, Mou, Xiaoyang, Zhang, Na, Zeng, Weiming, Li, Shasha, Yang, Yang
Formato: equations & formulas research tables/charts Journal Article
Publicado: Wiley-Blackwell 3/19/2015
Acceso en línea:Ver este registro en EBSCOhost
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      dt: 3/19/2015
      vid: 2015
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      pub: Wiley-Blackwell
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        10.1155/2015/394260
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        atl: The Construction of Common and Specific Significance Subnetworks of Alzheimer’s Disease from Multiple Brain Regions.
      aug:
        au:
          Kong, Wei
          Mou, Xiaoyang
          Zhang, Na
          Zeng, Weiming
          Li, Shasha
          Yang, Yang
        affil: Information Engineering College, Shanghai Maritime University, Shanghai 201306, China
      sug:
        subj:
          Alzheimer's Disease Physiopathology
          Brain Pathology
          Algorithms
          Genes
          Gene Expression
          Brain Anatomy and Histology
          Prospective Studies
          Resource Databases, Health
          Human
          Funding Source
      ab: Alzheimer’s disease (AD) is a progressively and fatally neurodegenerative disorder and leads to irreversibly cognitive and memorial damage in different brain regions. The identification and analysis of the dysregulated pathways and subnetworks among affected brain regions will provide deep insights for the pathogenetic mechanism of AD. In this paper, commonly and specifically significant subnetworks were identified from six AD brain regions. Protein-protein interaction (PPI) data were integrated to add molecular biological information to construct the functional modules of six AD brain regions by Heinz algorithm. Then, the simulated annealing algorithm based on edge weight is applied to predicting and optimizing the maximal scoring networks for common and specific genes, respectively, which can remove the weak interactions and add the prediction of strong interactions to increase the accuracy of the networks. The identified common subnetworks showed that inflammation of the brain nerves is one of the critical factors of AD and calcium imbalance may be a link among several causative factors in AD pathogenesis. In addition, the extracted specific subnetworks for each brain region revealed many biologically functional mechanisms to understand AD pathogenesis.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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