Identification of interactions using model-based multifactor dimensionality reduction.

ABSTRACT:

Background

Common complex traits may involve multiple genetic and environmental factors and their interactions. Many methods have been proposed to identify these interaction effects, among them several machine learning and data mining methods. These are attractive for identifying interactions because they do not rely on specific genetic model assumptions. To handle the computational burden arising from an exhaustive search, including all possible combinations of factors, filter methods try to select promising factors in advance.

Methods

Model-based multifactor dimensionality reduction (MB-MDR), a semiparametric machine learning method allowing adjustment for confounding variables and lower level effects, is applied to Genetic Analysis Workshop 19 (GAW19) data to identify int

SUBMITTER: Gola D 

PROVIDER: S-EPMC5133504 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature