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Open AccessArticle

A Pathway-Based Kernel Boosting Method for Sample Classification Using Genomic Data

1
Department of Biostatistics, Yale University, New Haven, CT 06511, USA
2
Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA
*
Author to whom correspondence should be addressed.
Genes 2019, 10(9), 670; https://doi.org/10.3390/genes10090670
Received: 12 August 2019 / Revised: 25 August 2019 / Accepted: 28 August 2019 / Published: 31 August 2019
(This article belongs to the Special Issue Statistical Methods for the Analysis of Genomic Data)
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 informative 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. View Full-Text
Keywords: classification; gene set enrichment analysis; boosting; kernel method classification; gene set enrichment analysis; boosting; kernel method
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Zeng, L.; Yu, Z.; Zhao, H. A Pathway-Based Kernel Boosting Method for Sample Classification Using Genomic Data. Genes 2019, 10, 670.

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