Semi-supervised empirical Bayes group-regularized factor regression

TL;DR

This paper introduces a semi-supervised Bayesian factor regression method that incorporates feature annotations via empirical Bayes, improving high-dimensional biomedical prediction models with unlabeled data.

Contribution

It develops a novel empirical Bayes approach for semi-supervised factor regression that leverages feature annotations to enhance prediction accuracy in high-dimensional settings.

Findings

Effective in simulations and real data applications

Improves prediction accuracy with feature annotations

Computationally efficient via Variational approximations

Abstract

The features in high dimensional biomedical prediction problems are often well described with lower dimensional manifolds. An example is genes that are organised in smaller functional networks. The outcome can then be described with the factor regression model. A benefit of the factor model is that is allows for straightforward inclusion of unlabeled observations in the estimation of the model, i.e., semi-supervised learning. In addition, the high dimensional features in biomedical prediction problems are often well characterised. Examples are genes, for which annotation is available, and metabolites with $p$-values from a previous study available. In this paper, the extra information on the features is included in the prior model for the features. The extra information is weighted and included in the estimation through empirical Bayes, with Variational approximations to speed up the computation. The method is demonstrated in simulations and two applications. One application considers influenza vaccine efficacy prediction based on microarray data. The second application predictions oral cancer metastatsis from RNAseq data.