Combining pathway identification and survival prediction via screening-network analysis
Motivation
Gene expression data from high-throughput assays, such as microarray, are often used to
predict cancer survival. However, available datasets consist of a small number of samples (n patients)
and a large number of gene expression data (p predictors). Therefore, the main challenge
is to cope with the high-dimensionality, i.e. p>>n, and a novel appealing approach is to use
screening procedures to reduce the size of the feature space to a moderate scale (Wu & Yin 2015,
Song et al. 2014, He et al. 2013).