Semi-supervised empirical Bayes group-regularized factor regression.
Biom J
; 64(7): 1289-1306, 2022 10.
Article
em En
| MEDLINE
| ID: mdl-35730912
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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Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Projetos de Pesquisa
/
Algoritmos
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
Biom J
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Holanda