Title: A pathway-based kernel boosting method for sample classification using genomic data,
Abstract: The analysis of cancer genomic data has long suffered "the curse of
dimensionality". Sample sizes for most cancer genomic studies are a few
hundreds at most while there are tens of thousands of genomic features studied.
Various methods have been proposed to leverage prior biological knowledge, such
as pathways, to more effectively analyze cancer genomic data. Most of the
methods focus on testing marginal significance of the associations between
pathways and clinical phenotypes. They can identify relevant pathways, but do
not involve predictive modeling. In this article, we propose a Pathway-based
Kernel Boosting (PKB) method for integrating gene pathway information for
sample classification, where we use kernel functions calculated from each
pathway as base learners and learn the weights through iterative optimization
of the classification loss function. We apply PKB and several competing methods
to three cancer studies with pathological and clinical information, including
tumor grade, stage, tumor sites, and metastasis status. Our results show that
PKB outperforms other methods, and identifies pathways relevant to the outcome
variables.