| In recent years, a number of computational and statistical problems for identifying SNP-SNP interactions in high dimensional survival data have been studied, and several data mining approaches have been proposed. However, the relative performance of these methods to detect SNP-SNP interactions has not been thoroughly investigated.;The results of this study demonstrate how the methods perform in detecting gene-gene interactions for survival data, and are useful in informing researchers about choosing an analysis tool for their own real data applications.;In this study, we directly compared the performance of the four techniques to detect gene-gene interactions in a recently conducted study of genetic polymorphisms associated with breast cancer survival and recurrence. Four methods were evaluated for their ability to detect SNP-SNP interactions: Survival Multifactor Dimensionality Reduction, Cox regression with L1 (Lasso) and L1-L2 (Elastic Net) penalties, and Random Survival Forest (RSF). Methods were contrasted on the basis of which SNPs they selected. |
