Yaohua Rong, Sihai Dave Zhao, Ji Zhu, Wei Yuan, Weihu Cheng, Yi Li
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...
2018: Statistics and its Interface