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C. Coffey, P. Hebert, M. Ritchie, H. Krumholz, J. Gaziano, P. Ridker, N. Brown, D. Vaughan and J. Moore, “An Application of Conditional Logistic Regression and Multifactor Dimensionality Reduction for Detecting Gene- Gene Interactions on Risk of Myocardial Infarction: The Importance of Model Validation,” BMC Bioinformatics, Vol. 5, 2004, p. 49. doi:10.1186/1471-2105-5-49

has been cited by the following article:

• TITLE: Statistical Methods of SNP Data Analysis and Applications

  AUTHORS: Alexander Bulinski, Oleg Butkovsky, Victor Sadovnichy, Alexey Shashkin, Pavel Yaskov, Alexander Balatskiy, Larisa Samokhodskaya, Vsevolod Tkachuk

  KEYWORDS: Genetic Data Statistical Analysis; Multifactor Dimensionality Reduction; Ternary Logic Regression; Random Forests; Stochastic Gradient Boosting; Independent Rule; Single Nucleotide Polymorphisms; Coronary Heart Disease; Myocardial Infarction

  JOURNAL NAME: Open Journal of Statistics, Vol.2 No.1, January 6, 2012

  ABSTRACT: We develop various statistical methods important for multidimensional genetic data analysis. Theorems justifying application of these methods are established. We concentrate on the multifactor dimensionality reduction, logic regression, random forests, stochastic gradient boosting along with their new modifications. We use complementary approaches to study the risk of complex diseases such as cardiovascular ones. The roles of certain combinations of single nucleotide polymorphisms and non-genetic risk factors are examined. To perform the data analysis concerning the coronary heart disease and myocardial infarction the Lomonosov Moscow State University supercomputer “Chebyshev” was employed.