A key step in pharmacogenomic studies is the development of accurate prediction models for drug response based on individuals’ genomic information. Recent interest has centered on semiparametric models based on kernel machine regression, which can flexibly model the complex relationships between gene expression and drug response. However, performance suffers if irrelevant covariates are unknowingly included when training the model. We propose a new semiparametric regression procedure, based on a novel penalized garrotized kernel machine (PGKM), which can better adapt to the presence of irrelevant covariates while still allowing for a complex nonlinear model and gene-gene interactions. We study the performance of our approach in simulations and in a pharmacogenomic study of the renal carcinoma drug temsirolimus. Our method predicts plasma concentration of temsirolimus as well as standard kernel machine regression when no irrelevant covariates are included in training, but has much higher prediction accuracy when the truly important covariates are not known in advance. Supplemental materials, including R code used in this manuscript, are available online.
Growing interest in genomics research has called for new semiparametric models based on kernel machine regression for modeling health outcomes. Models containing redundant predictors often show unsatisfactory prediction performance. Thus, our task is to construct a method which can guarantee the estimation accuracy by removing redundant variables. Specifically, in this paper, based on the regularization method and an innovative class of garrotized kernel functions, we propose a novel penalized kernel machine method for a semiparametric logistic model. Our method can promise us high prediction accuracies, due to its capability of flexibly describing the complicated relationship between responses and predictors and its compatibility of the interactions among the predictors. In addition, our method can also remove the redundant variables. Our numerical experiments demonstrate that our method yields higher prediction accuracies compared to competing approaches. Supplementary materials for this paper are available online.
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