Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies
@article{Friedrichs2017PathwayBasedKB, title={Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies}, author={Stefanie Friedrichs and Juliane Manitz and Patricia Burger and Christopher I. Amos and Angela Risch and Jenny Chang-Claude and Heinz-Erich Wichmann and Thomas Kneib and Heike Bickeb{\"o}ller and Benjamin Hofner}, journal={Computational and Mathematical Methods in Medicine}, year={2017}, volume={2017} }
The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as baseโฆย
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