Google goes cancer: improving outcome prediction for cancer patients by network-based ranking of marker genes.

ABSTRACT: Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner simi

SUBMITTER: Winter C 

PROVIDER: S-EPMC3355064 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature