Kernel based methods for accelerated failure time model with ultra-high dimensional data

Background: Most genomic data have ultra-high dimensions with more than 10,000 genes (probes). Regularization methods with L1 and Lp penalty have been extensively studied in survival analysis with high-dimensional genomic data. However, when the sample size n m (the number of genes), directly identifying a small subset of genes from ultra-high (m > 10, 000) dimensional data is time-consuming and not computationally efficient. In current microarray analysis, what people really do is select a couple of thousands (or hundreds) of genes using univariate analysis or statistical tests, and then apply the LASSO-type penalty to further reduce the number of disease associated genes. This two-step procedure may introduce bias and inaccuracy and lead us to miss biologically important genes. Results: The accelerated failure time (AFT) model is a linear regression model and a useful alternative to the Cox model for survival analysis. In this paper, we propose a nonlinear kernel based AFT model...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Zhenqiu Liu, Dechang Chen, Ming Tan, Feng Jiang, Ronald B. Gartenhaus
• Keywords: BMCBI 2010, Business, Genomic Data, Survival Analysis, Variable Selection Method