Identification of genetic interaction networks via an evolutionary algorithm evolved Bayesian network.

ABSTRACT:

Background

The future of medicine is moving towards the phase of precision medicine, with the goal to prevent and treat diseases by taking inter-individual variability into account. A large part of the variability lies in our genetic makeup. With the fast paced improvement of high-throughput methods for genome sequencing, a tremendous amount of genetics data have already been generated. The next hurdle for precision medicine is to have sufficient computational tools for analyzing large sets of data. Genome-Wide Association Studies (GWAS) have been the primary method to assess the relationship between single nucleotide polymorphisms (SNPs) and disease traits. While GWAS is sufficient in finding individual SNPs with strong main effects, it does not capture potential interactions amon

SUBMITTER: Li R 

PROVIDER: S-EPMC4862166 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature