VEPAD - Predicting the effect of variants associated with Alzheimer's disease using machine learning.

Journal: Computers in biology and medicine
Published Date: Aug 5, 2020

Abstract

INTRODUCTION: Alzheimer's disease (AD) is a complex and heterogeneous disease that affects neuronal cells over time and it is prevalent among all neurodegenerative diseases. Next Generation Sequencing (NGS) techniques are widely used for developing high-throughput screening methods to identify biomarkers and variants, which help early diagnosis and treatments.

Authors

  • Uday Rangaswamy
    Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, 600036, India.
  • S Akila Parvathy Dharshini
    Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, 600036, India.
  • Dhanusha Yesudhas
    Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, 600036, India.
  • M Michael Gromiha
    Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India; International Research Frontiers Initiative, School of Computing, Tokyo Institute of Technology, Yokohama, Japan. Electronic address: gromiha@iitm.ac.in.

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