Sequence Analysis Using Logic Regression

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Iterative random forests to discover predictive and stable high-order interactions (2017, methodology): “Popular statistical and machine-learning methods for detecting interactions among features include decision trees and their ensembles: CART (12), random forests (RFs) (13), Node Harvest (14), Forest Garrote (15), and Rulefit3 (16), as well as methods more specific to gene–gene interactions with cate”
Detecting gene-gene interactions that underlie human diseases (2009, methodology): “A range of data-mining approaches have been used for the detection of interactions or potentially interacting variables in genetic association studies, including logic regressio”
Screening large-scale association study data: exploiting interactions using random forests (2004, methodology): “A related adaptive regression method has also shown promise in selecting a small number of predictive SNPs from a set of hundreds of potential predictors [18].”
The Ubiquitous Nature of Epistasis in Determining Susceptibility to Common Human Diseases (2003, methodology): “The use of logic functions for defining new variables that can be included in a logistic regression analysis may also be useful [Kooperberg et al., 2001].”