Dissection of gene expression datasets into clinically relevant interaction signatures via high-dimensional correlation maximization

Abstract

Identification of clinically relevant gene expression signatures for cancer stratification remains challenging. Here, the authors introduce a flexible nonlinear signal superposition model that enables dissection of large gene expression data sets into signatures and extraction of gene interactions.