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

ABSTRACT:

Background

Most genomic data have ultra-high dimensions with more than 10,000 genes (probes). Regularization methods with L₁ and L(p) 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 gene

SUBMITTER: Liu Z 

PROVIDER: S-EPMC3019227 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature