ArticleBiometrics2018
Scalable Bayesian variable selection for structured high-dimensional data.
Article in Biometrics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Simultaneous Representation Learning of Multi-Omics and Clinical Outcome Data via a Supervised Knowledge-Guided Bayesian Factor Model.Statistics in medicine · 2026Article
- Bayesian thresholded modeling for integrating brain node and network predictors.Biostatistics (Oxford, England) · 2025Article
- Bayesian mixed model inference for genetic association under related samples with brain network phenotype.Biostatistics (Oxford, England) · 2024Article
- Graph-guided Bayesian Factor Model for Integrative Analysis of Multi-modal Data with Noisy Network Information.Statistics in biosciences · 2024Article
- Group lasso priors for Bayesian accelerated failure time models with left-truncated and interval-censored data.Statistical methods in medical research · 2024Article
- Single-cell biclustering for cell-specific transcriptomic perturbation detection in AD progression.Cell reports methods · 2024Article
- Incorporating graph information in Bayesian factor analysis with robust and adaptive shrinkage priors.Biometrics · 2024Article
- Accounting for network noise in graph-guided Bayesian modeling of structured high-dimensional data.Biometrics · 2024Article
- Integrative Learning of Structured High-Dimensional Data from Multiple Datasets.Statistical analysis and data mining · 2023Article
- Integrative analysis of multi-omics and imaging data with incorporation of biological information via structural Bayesian factor analysis.Briefings in bioinformatics · 2023Article
- Genetic underpinnings of brain structural connectome for young adults.Journal of the American Statistical Association · 2023Article
- Integrative Bayesian tensor regression for imaging genetics applications.Frontiers in neuroscience · 2023Article
- Semi-parametric Bayes regression with network-valued covariates.Machine learning · 2022Article
- Bayesian sparse heritability analysis with high-dimensional neuroimaging phenotypes.Biostatistics (Oxford, England) · 2022Article
- Graph-guided Bayesian SVM with Adaptive Structured Shrinkage Prior for High-dimensional Data.Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data · 2021Article
- Bayesian generalized biclustering analysis via adaptive structured shrinkage.Biostatistics (Oxford, England) · 2020Article
- Bayesian Network Marker Selection via the Thresholded Graph Laplacian Gaussian Prior.Bayesian analysis · 2020Article
- GRIA: Graphical Regularization for Integrative Analysis.Proceedings of the ... SIAM International Conference on Data Mining. SIAM International Conference on Data Mining · 2020Article
- Article
- Bayesian Non-linear Support Vector Machine for High-Dimensional Data with Incorporation of Graph Information on Features.Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Variable selection for structured covariates lying on an underlying known graph is a problem motivated by practical applications, and has been a topic of increasing interest. However, most of the existing methods may not be scalable to high-dimensional settings involving tens of thousands of variables lying on known pathways such as the case in genomics studies. We propose an adaptive Bayesian shrinkage approach which incorporates prior network information by smoothing the shrinkage parameters for connected variables in the graph, so that the corresponding coefficients have a similar degree of shrinkage. We fit our model via a computationally efficient expectation maximization algorithm which scalable to high-dimensional settings (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.