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

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.

SUBMITTER: Jamal S 

PROVIDER: S-EPMC5070370 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature