A Regularized Hierarchical Model for Incorporating Annotation Information in Predictive Omic Studies

Music City Center 

Associated with high-dimensional omics data there are often "meta-features" such as biological pathways and functional annotations that can be informative for predicting an outcome of interest. We introduce a regularized hierarchical framework for integrating meta-features, with the goal of improving prediction and feature selection performance with time-to-event outcomes.
A hierarchical framework is deployed to incorporate meta-features. Regularization is applied to the omic features as well as the meta-features so that high-dimensional data can be handled at both levels. The proposed hierarchical Cox model can be efficiently fitted by a combination of iterative reweighted least squares and cyclic coordinate descent.
Simulations show that when the external meta-features are informative, the regularized hierarchical model can substantially improve prediction performance over standard regularized Cox regression. We illustrate the proposed model with applications to breast cancer and melanoma survival based on gene expression profiles, which show the improvement in prediction performance by applying meta-features, as well as the discovery of important omic feature sets.

integrated analysis

omics

meta-feature

precision medicine

regularized regression

hierarchical model 

Section on Statistics in Genomics and Genetics