Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions

By Lance W. Hahn, Marylyn DeRiggi Ritchie, Jason H. Moore. Bioinform., 2003

1,147 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/721268ea924df18f5a262d1974479f00623f6370

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In Genetics

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Citations in the corpus

AI in Health: State of the Art, Challenges, and Future Directions (2019, methodology): “[82] developed multifactor dimensionality reduction software for detecting gene–gene and gene–environment interactions.”
Power of multifactor dimensionality reduction for detecting gene‐gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity (2003, methodology): “Additionally, a theoretical study provided proof that MDR is ideally suited for discriminating between binary clinical endpoints using multilocus genotypes [Hahn and Moore, unpublished findings], and a software package is available [Hahn et al., 2003].”
Discovering causal interactions using Bayesian network scoring and information gain (2016, methodology): “A well-known new technique called Multifactor Dimensionality Reduction ( MDR ) [33] was also developed.”
A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction (2007, methodology): “…of one to five SNPs. Five-fold cross validation [Motsinger and Ritchie, 2006] was used to select an MDR model with maximum testing accuracy (i.e. most likely to generalize) and maximum cross-validation consistency as described previously [Ritchie et al., 2001, 2003; Hahn et al., 2003; Moore, 2004].”
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.”
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”