ArticleBiometrical journal. Biometrische Zeitschrift2022
Semi-supervised empirical Bayes group-regularized factor regression.
Article in Biometrical journal. Biometrische Zeitschrift, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Improving Genomic Prediction Using High-Dimensional Secondary Phenotypes: The Genetic Latent Factor Approach.Biometrical journal. Biometrische Zeitschrift · 2025Article
- Semi-supervised empirical Bayes group-regularized factor regression.Biometrical journal. Biometrische Zeitschrift · 2022Article
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4 authors.
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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 empirical Bayes. The method is demonstrated in simulations and two applications. One application considers influenza vaccine efficacy prediction based on microarray data. The second application predicts oral cancer metastasis from RNAseq data.
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