Reference : Survival analysis: finding relevant epistatic SNP pairs using Model- Based Multifacto...
Scientific congresses and symposiums : Unpublished conference/Abstract
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/142672
Survival analysis: finding relevant epistatic SNP pairs using Model- Based Multifactor Dimensionality Reduction
English
Van Lishout, François mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Bioinformatique >]
Vens, Céline mailto [K.U. Leuven > Computer Science Department > > >]
Urrea, Victor [University of Vic > Departement of Systems Biology > > >]
Calle, M. Luz [University of Vic > Departement of Systems Biology > > >]
Wehenkel, Louis mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Van Steen, Kristel mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Bioinformatique >]
3-Dec-2012
Yes
No
International
5th International Conference of the ERCIM WG on COMPUTING & STATISTICS
1-3 December 2012
Oviedo
Spain
[en] Model-Based Multifactor Dimensionality Reduction ; Epistasis ; Survival analysis
[en] Analyzing the combined effects of genes (and/or environmental factors) on the development of complex diseases is quite challenging, both from the statistical and computational perspective, even using a relatively small number of genetic and non-genetic exposures. Several data-mining methods have been proposed for interaction analysis, among them, the Multifactor Dimensionality Reduction Method (MDR). Model-Based Multifactor Dimensionality Reduction (MB-MDR), a relatively new dimensionality reduction technique, is able to unify the best of both nonparametric and parametric worlds, and has proven its utility in a variety of theoretical and practical settings. Until now, MB-MDR software has only accommodated traits that are measured on a binary or interval scale. Time-to-event data could therefore not be analyzed with the MB-MDR methodology. MB-MDR-3.0.0 overcomes this shortcoming of earlier versions. We show the added value of MB-MDR for censored traits by comparing the implemented strategies with more classical methods such as those based on a parametric regression paradigm. The simulation results are supplemented with an application to real-life data.
http://hdl.handle.net/2268/142672

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