Bootstrap aggregating of alternating decision trees to detect sets of SNPs that associate with disease.

ABSTRACT: Complex genetic disorders are a result of a combination of genetic and nongenetic factors, all potentially interacting. Machine learning methods hold the potential to identify multilocus and environmental associations thought to drive complex genetic traits. Decision trees, a popular machine learning technique, offer a computationally low complexity algorithm capable of detecting associated sets of single nucleotide polymorphisms (SNPs) of arbitrary size, including modern genome-wide SNP scans. However, interpretation of the importance of an individual SNP within these trees can present challenges. We present a new decision tree algorithm denoted as Bagged Alternating Decision Trees (BADTrees) that is based on identifying common structural elements in a bootstrapped set of Alternating Deci

SUBMITTER: Guy RT 

PROVIDER: S-EPMC3769952 | biostudies-literature | 2012 Feb

REPOSITORIES: biostudies-literature