SNPInterForest: a new method for detecting epistatic interactions.

ABSTRACT:

Background

Multiple genetic factors and their interactive effects are speculated to contribute to complex diseases. Detecting such genetic interactive effects, i.e., epistatic interactions, however, remains a significant challenge in large-scale association studies.

Results

We have developed a new method, named SNPInterForest, for identifying epistatic interactions by extending an ensemble learning technique called random forest. Random forest is a predictive method that has been proposed for use in discovering single-nucleotide polymorphisms (SNPs), which are most predictive of the disease status in association studies. However, it is less sensitive to SNPs with little marginal effect. Furthermore, it does not natively exhibit information on interaction patterns of suscepti

SUBMITTER: Yoshida M 

PROVIDER: S-EPMC3260223 | biostudies-literature | 2011 Dec

REPOSITORIES: biostudies-literature