Integrating network, sequence and functional features using machine learning approaches towards identification of novel Alzheimer genes.

Journal: BMC genomics
Published Date: Oct 18, 2016

Abstract

BACKGROUND: Alzheimer's disease (AD) is a complex progressive neurodegenerative disorder commonly characterized by short term memory loss. Presently no effective therapeutic treatments exist that can completely cure this disease. The cause of Alzheimer's is still unclear, however one of the other major factors involved in AD pathogenesis are the genetic factors and around 70 % risk of the disease is assumed to be due to the large number of genes involved. Although genetic association studies have revealed a number of potential AD susceptibility genes, there still exists a need for identification of unidentified AD-associated genes and therapeutic targets to have better understanding of the disease-causing mechanisms of Alzheimer's towards development of effective AD therapeutics.

Authors

  • Salma Jamal
    School of Biotechnology, Jawaharlal Nehru University, New Delhi-110067, India phone/fax: +91-11-26738728; fax: +91-11-26702040.
  • Sukriti Goyal
  • Asheesh Shanker
    Bioinformatics Programme, Centre for Biological Sciences, Central University of South Bihar, BIT Campus, Patna, Bihar, India.
  • Abhinav Grover
    Department of Pharmacology, Lady Hardinge Medical College and Associated Hospitals, New Delhi, India.

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