Revealing Alzheimer's disease genes spectrum in the whole-genome by machine learning.

Journal: BMC neurology
Published Date: Jan 10, 2018

Abstract

BACKGROUND: Alzheimer's disease (AD) is an important, progressive neurodegenerative disease, with a complex genetic architecture. A key goal of biomedical research is to seek out disease risk genes, and to elucidate the function of these risk genes in the development of disease. For this purpose, expanding the AD-associated gene set is necessary. In past research, the prediction methods for AD related genes has been limited in their exploration of the target genome regions. We here present a genome-wide method for AD candidate genes predictions.

Authors

  • Xiaoyan Huang
    Department of Cardiology, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang 310015, P.R. China.
  • Hankui Liu
    BGI-Shenzhen, Shenzhen, 518083, China.
  • Xinming Li
    College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China.
  • Liping Guan
    BGI-Shenzhen, Shenzhen, 518083, China.
  • Jiankang Li
    BGI-Shenzhen, Shenzhen, 518083, China.
  • Laurent Christian Asker M Tellier
    BGI-Shenzhen, Shenzhen, 518083, China.
  • Huanming Yang
    BGI-Shenzhen, Shenzhen 518083, China.
  • Jian Wang
    Veterinary Diagnostic Center, Shanghai Animal Disease Control Center, Shanghai, China.
  • Jianguo Zhang
    College of Automation, Harbin Engineering University, No. 145, Nantong street, Harbin, China.

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