Detecting Gene-Gene Interactions Using a Permutation-Based Random Forest Method

Authors

Jing Li, Dartmouth College
James D. Malley, National Institutes of Health
Angeline S. Andrew, Dartmouth CollegeFollow
Margaret R. Karagas, Dartmouth College
Jason H. Moore, University of Pennsylvania

Document Type

Article

Publication Date

4-6-2016

Publication Title

BioData Mining

Department

Geisel School of Medicine

Abstract

Identifying gene-gene interactions is essential to understand disease susceptibility and to detect genetic architectures underlying complex diseases. Here, we aimed at developing a permutation-based methodology relying on a machine learning method, random forest (RF), to detect gene-gene interactions. Our approach called permuted random forest (pRF) which identified the top interacting single nucleotide polymorphism (SNP) pairs by estimating how much the power of a random forest classification model is influenced by removing pairwise interactions.

DOI

10.1186/s13040-016-0093-5

Dartmouth Digital Commons Citation

Li, Jing; Malley, James D.; Andrew, Angeline S.; Karagas, Margaret R.; and Moore, Jason H., "Detecting Gene-Gene Interactions Using a Permutation-Based Random Forest Method" (2016). Dartmouth Scholarship. 545.
https://digitalcommons.dartmouth.edu/facoa/545