Semi-supervised empirical Bayes group-regularized factor regression.

ABSTRACT: The features in a high-dimensional biomedical prediction problem are often well described by low-dimensional latent variables (or factors). We use this to include unlabeled features and additional information on the features when building a prediction model. Such additional feature information is often available in biomedical applications. Examples are annotation of genes, metabolites, or p-values from a previous study. We employ a Bayesian factor regression model that jointly models the features and the outcome using Gaussian latent variables. We fit the model using a computationally efficient variational Bayes method, which scales to high dimensions. We use the extra information to set up a prior model for the features in terms of hyperparameters, which are then estimated through empiric

SUBMITTER: Munch MM 

PROVIDER: S-EPMC9796498 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature