Nonlinear network-based quantitative trait prediction from transcriptomic data

Understand

Quantitatively predicting phenotype variables by the expression changes in a set of candidate genes is of great interest in molecular biology but it is also a challenging task for several reasons.

• First, the collected biological observations might be heterogeneous and correspond to different biological mechanisms.
• Secondly, the gene expression variables used to predict the phenotype are potentially highly correlated since genes interact though unknown regulatory networks.
• In this paper, we present a novel approach designed to predict quantitative trait from transcriptomic data, taking into account the heterogeneity in biological samples and the hidden gene regulatory networks underlying different biological mechanisms.