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.
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). Open Dartmouth: Faculty Open Access Articles. 545.