Use of Deep-Learning Genomics to Discriminate Healthy Individuals from Those with Alzheimer's Disease or Mild Cognitive Impairment.

Journal: Behavioural neurology
Published Date: Jul 14, 2021

Abstract

OBJECTIVES: Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the elderly. Certain genes have been identified as important clinical risk factors for AD, and technological advances in genomic research, such as genome-wide association studies (GWAS), allow for analysis of polymorphisms and have been widely applied to studies of AD. However, shortcomings of GWAS include sensitivity to sample size and hereditary deletions, which result in low classification and predictive accuracy. Therefore, this paper proposes a novel deep-learning genomics approach and applies it to multitasking classification of AD progression, with the goal of identifying novel genetic biomarkers overlooked by traditional GWAS analysis.

Authors

  • Lanlan Li
    Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China.
  • Yeying Yang
    LongHua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200032, China.
  • Qi Zhang
    Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Jiao Wang
    Key Lab of Cell Differentiation and Apoptosis of Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Jiehui Jiang
    Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China.
  • Alzheimer's Disease Neuroimaging Initiative
    Indiana University School of Medicine, Indianapolis, IN 46202, USA.

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