Network-based survival analysis reveals subnetwork signatures for predicting outcomes of ovarian cancer treatment.

ABSTRACT: Cox regression is commonly used to predict the outcome by the time to an event of interest and in addition, identify relevant features for survival analysis in cancer genomics. Due to the high-dimensionality of high-throughput genomic data, existing Cox models trained on any particular dataset usually generalize poorly to other independent datasets. In this paper, we propose a network-based Cox regression model called Net-Cox and applied Net-Cox for a large-scale survival analysis across multiple ovarian cancer datasets. Net-Cox integrates gene network information into the Cox's proportional hazard model to explore the co-expression or functional relation among high-dimensional gene expression features in the gene network. Net-Cox was applied to analyze three independent gene expression da

SUBMITTER: Zhang W 

PROVIDER: S-EPMC3605061 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature