Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer.

By Marylyn DeRiggi Ritchie, Lance W. Hahn, Nady Roodi, L. Bailey, W. Dupont, F. Parl, Jason H. Moore. 2001

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Citations in the corpus

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”
A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction (2022, methodology): “For higher-order interactions, exhaustive ﬁ lter methods have been developed (e.g., Multifactor Dimensionality Reduction (MDR) (Ritchie et al., 2001) or the Combinatorial Partitioning Method (CPM) (Nelson et al., 2001)) and shown to be able to detect SNP-SNP interactions across ≥ 3 orders.”
MegaSNPHunter: a learning approach to detect disease predisposition SNPs and high level interactions in genome wide association study (2009, methodology): “For this small set of SNPs, the multi-SNP interactions could be extracted and ranked even using the brute-force search method like MDR.”
Traversing the conceptual divide between biological and statistical epistasis: systems biology and a more modern synthesis. (2005, background): “The multifactor dimensionality reduction approach (MDR) has identified significant evidence of epistasis in sporadic breast cancer,((38)) essential hypertension,((52)) atrial fibrillation((53)) and type II diabetes.”
Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix (2005, methodology): “As they are caused by interactions among genes, an increasing number of studies (Hoh et al, 2001; Nelson et al, 2001; Ritchie et al, 2001; Cordell and Clayton, 2002) have employed multilocus methods to detect the causative genes.”
A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. (2006, methodology): “In response to the need for powerful analytical strategies for detecting epistasis we have previously developed a nonparametric and genetic model-free data mining method called multifactor dimensionality reduction (MDR) (Ritchie et al., 2001; Hahn et al., 2003; Ritchie et al., 2003a, b; Hahn and Moo”
Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions (2003, methodology): “Further, MDR has played an important role in the identiﬁcation of gene–gene interactions in real data from case-control studies of sporadic breast cancer (Ritchie et al. , 2001) and essential hypertension (Moore and Williams, 2002).”
Discovering causal interactions using Bayesian network scoring and information gain (2016, positive): “MDR has been applied to detect epistatically interacting loci in hypertension [34], sporadic breast cancer [35], and type II diabetes [36].”
Screening large-scale association study data: exploiting interactions using random forests (2004, methodology): “Combinatorial partitioning and multifactor dimensionality reduction [7-10] are closely related methods developed specifically to detect higher-order interactions among polymorphisms that predict trait variation.”
The Ubiquitous Nature of Epistasis in Determining Susceptibility to Common Human Diseases (2003, methodology): “Summary of the general steps involved in implementing the MDR method [adapted from Ritchie et al., 2001].”
Bayesian inference of epistatic interactions in case-control studies (2007, methodology): “genotype combinations across all markers in M, Pjoin (X), as in equation (2).”