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 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.
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Bibliographic citation
-
Semi‐supervised empirical Bayes group‐regularized factor regression ; day:22 ; month:06 ; year:2022 ; extent:18
Biometrical journal ; (22.06.2022) (gesamt 18)
- Creator
-
Münch, Magnus M.
van de Wiel, Mark A.
van der Vaart, Aad W.
Peeters, Carel F. W.
- DOI
-
10.1002/bimj.202100105
- URN
-
urn:nbn:de:101:1-2022062215382232237112
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:21 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Münch, Magnus M.
- van de Wiel, Mark A.
- van der Vaart, Aad W.
- Peeters, Carel F. W.
Other Objects (12)
High-Rankness Regularized Semi-Supervised Deep Metric Learning for Remote Sensing Imagery
Autoencoding slow representations for semi-supervised data-efficient regression
Semi-supervised data cleaning
Incremental predictive clustering trees for online semi-supervised multi-target regression
Semi-supervised structured prediction models
Semi-supervised protein subcellular localization
DOLDA: a regularized supervised topic model for high-dimensional multi-class regression
Semi-supervised learning for image classification
A survey on semi-supervised learning
Semi-supervised classifier guided by discriminator
Semi-supervised discovery of differential genes
Feature ranking for semi-supervised learning
High-Rankness Regularized Semi-Supervised Deep Metric Learning for Remote Sensing Imagery
Autoencoding slow representations for semi-supervised data-efficient regression
Semi-supervised data cleaning
Incremental predictive clustering trees for online semi-supervised multi-target regression
Semi-supervised structured prediction models
Semi-supervised protein subcellular localization
DOLDA: a regularized supervised topic model for high-dimensional multi-class regression
Semi-supervised learning for image classification
A survey on semi-supervised learning
Semi-supervised classifier guided by discriminator
Semi-supervised discovery of differential genes
Feature ranking for semi-supervised learning
High-Rankness Regularized Semi-Supervised Deep Metric Learning for Remote Sensing Imagery
Autoencoding slow representations for semi-supervised data-efficient regression
Semi-supervised data cleaning
Incremental predictive clustering trees for online semi-supervised multi-target regression
Semi-supervised structured prediction models
Semi-supervised protein subcellular localization
DOLDA: a regularized supervised topic model for high-dimensional multi-class regression
Semi-supervised learning for image classification
A survey on semi-supervised learning
Semi-supervised classifier guided by discriminator
Semi-supervised discovery of differential genes