TY - JOUR AU - Liu, Zhenqiu AU - Chen, Dechang AU - Tan, Ming AU - Jiang, Feng AU - Gartenhaus, Ronald B. PY - 2010 DA - 2010/12/21 TI - Kernel based methods for accelerated failure time model with ultra-high dimensional data JO - BMC Bioinformatics SP - 606 VL - 11 IS - 1 AB - Most genomic data have ultra-high dimensions with more than 10,000 genes (probes). Regularization methods with L1 and Lppenalty 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. SN - 1471-2105 UR - https://doi.org/10.1186/1471-2105-11-606 DO - 10.1186/1471-2105-11-606 ID - Liu2010 ER -