A data-driven interactome of synergistic genes improves network-based cancer outcome prediction.

ABSTRACT: Robustly predicting outcome for cancer patients from gene expression is an important challenge on the road to better personalized treatment. Network-based outcome predictors (NOPs), which considers the cellular wiring diagram in the classification, hold much promise to improve performance, stability and interpretability of identified marker genes. Problematically, reports on the efficacy of NOPs are conflicting and for instance suggest that utilizing random networks performs on par to networks that describe biologically relevant interactions. In this paper we turn the prediction problem around: instead of using a given biological network in the NOP, we aim to identify the network of genes that truly improves outcome prediction. To this end, we propose SyNet, a gene network constructed ab i

SUBMITTER: Allahyar A 

PROVIDER: S-EPMC6380593 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature