首页    期刊浏览 2024年11月26日 星期二
登录注册

文章基本信息

  • 标题:Alzheimer’s Disease under the Purview of Graph Theory Centric Genetic Networks
  • 本地全文:下载
  • 作者:Yegnanarayanan Venkatraman ; Krithicaa Narayanaa Y ; Valentina E.Balas
  • 期刊名称:Brain. Broad Research in Artificial Intelligence and Neuroscience
  • 印刷版ISSN:2067-3957
  • 出版年度:2021
  • 卷号:12
  • 期号:2
  • 页码:178-201
  • DOI:10.18662/brain/12.2/199
  • 语种:English
  • 出版社:EduSoft publishing
  • 摘要:Notice that the synapsis of brain is a form of communication. As communication demands connectivity, it is not a surprise that "graph theory" is a fastest growing area of research in the life sciences. It attempts to explain the connections and communication between networks of neurons. Alzheimer’s disease (AD) progression in brain is due to a deposition and development of amyloid plaque and the loss of communication between nerve cells. Graph/network theory can provide incredible insights into the incorrect wiring leading to memory loss in a progressive manner. Network in AD is slanted towards investigating the intricate patterns of interconnections found in the pathogenesis of brain. Here, we see how the notions of graph/network theory can be prudently exploited to comprehend the Alzheimer’s disease. We begin with introducing concepts of graph/network theory as a model for specific genetic hubs of the brain regions and cellular signalling. We begin with a brief introduction of prevalence and causes of AD followed by outlining its genetic and signalling pathogenesis. We then present some of the network-applied outcome in assessing the disease-signalling interactions, signal transduction of protein-protein interaction, disturbed genetics and signalling pathways as compelling targets of pathogenesis of the disease.
  • 关键词:Alzheimer’s disease;Cell signalling networks;Genetic networks;Graph Centrality measures;Characteristic path length;Clustering coefficient
国家哲学社会科学文献中心版权所有