Robust phenotype prediction from gene expression data using differential shrinkage of co-regulated genes.

ABSTRACT: Discovery of robust diagnostic or prognostic biomarkers is a key to optimizing therapeutic benefit for select patient cohorts - an idea commonly referred to as precision medicine. Most discovery studies to derive such markers from high-dimensional transcriptomics datasets are weakly powered with sample sizes in the tens of patients. Therefore, highly regularized statistical approaches are essential to making generalizable predictions. At the same time, prior knowledge-driven approaches have been successfully applied to the manual interpretation of high-dimensional transcriptomics datasets. In this work, we assess the impact of combining two orthogonal approaches for the discovery of biomarker signatures, namely (1) well-known lasso-based regression approaches and its more recent derivative

SUBMITTER: Zarringhalam K 

PROVIDER: S-EPMC5775343 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature