Incorporating prior knowledge into regularized regression.

ABSTRACT:

Motivation

Associated with genomic features like gene expression, methylation and genotypes, used in statistical modeling of health outcomes, there is a rich set of meta-features like functional annotations, pathway information and knowledge from previous studies, that can be used post hoc to facilitate the interpretation of a model. However, using this meta-feature information a priori rather than post hoc can yield improved prediction performance as well as enhanced model interpretation.

Results

We propose a new penalized regression approach that allows a priori integration of external meta-features. The method extends LASSO regression by incorporating individualized penalty parameters for each regression coefficient. The penalty parameters are, in turn, modeled as a log-l

SUBMITTER: Zeng C 

PROVIDER: S-EPMC8599719 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature