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
Included in
Bioinformatics Commons, Computational Biology Commons, Diseases Commons, Genetics Commons