ArticleJournal of the Royal Statistical Society. Series C, Applied statistics2014
Bayesian hierarchical structured variable selection methods with application to MIP studies in breast cancer.
Article in Journal of the Royal Statistical Society. Series C, Applied statistics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- MixedBayes: An R Package for Longitudinal Gene-Environment Interaction Analysis Using Robust Sparse Bayesian Mixed Models.Entropy (Basel, Switzerland) · 2026Article
- Multivariate Bayesian variable selection for multi-trait genetic fine mapping.Journal of the Royal Statistical Society. Series C, Applied statistics · 2025Article
- Is Seeing Believing? A Practitioner's Perspective on High-Dimensional Statistical Inference in Cancer Genomics Studies.Entropy (Basel, Switzerland) · 2024Review
- A hierarchical spike-and-slab model for pan-cancer survival using pan-omic data.BMC bioinformatics · 2022Article
- Bayesian sparse heritability analysis with high-dimensional neuroimaging phenotypes.Biostatistics (Oxford, England) · 2022Article
- Bayesian hierarchical models for high-dimensional mediation analysis with coordinated selection of correlated mediators.Statistics in medicine · 2021Article
- Variable selection and estimation in causal inference using Bayesian spike and slab priors.Statistical methods in medical research · 2020Article
- A Bayesian hierarchical variable selection prior for pathway-based GWAS using summary statistics.Statistics in medicine · 2020Article
- Semiparametric Bayesian variable selection for gene-environment interactions.Statistics in medicine · 2020Article
- Knowledge-Guided Statistical Learning Methods for Analysis of High-Dimensional -Omics Data in Precision Oncology.JCO precision oncology · 2019Review
- Bayesian Joint Spike-and-Slab Graphical Lasso.Proceedings of machine learning research · 2019Article
- Structured Genome-Wide Association Studies with Bayesian Hierarchical Variable Selection.Genetics · 2019Article
- Bayesian variable selection based on clinical relevance weights in small sample studies-Application to colon cancer.Statistics in medicine · 2019Article
- A Selective Review of Multi-Level Omics Data Integration Using Variable Selection.High-throughput · 2019Review
- Using Regularization to Infer Cell Line Specificity in Logical Network Models of Signaling Pathways.Frontiers in physiology · 2018Article
- A Bayesian Gene-Based Genome-Wide Association Study Analysis of Osteosarcoma Trio Data Using a Hierarchically Structured Prior.Cancer informatics · 2018Article
- Bayesian hierarchical modelling for inferring genetic interactions in yeast.Journal of the Royal Statistical Society. Series C, Applied statistics · 2016Article
- Elucidation of Genetic Interactions in the Yeast GATA-Factor Network Using Bayesian Model Selection.PLoS computational biology · 2016Article
- Bayesian joint selection of genes and pathways: applications in multiple myeloma genomics.Cancer informatics · 2014Article
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7 authors.
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Abstract
The analysis of alterations that may occur in nature when segments of chromosomes are copied (known as copy number alterations) has been a focus of research to identify genetic markers of cancer. One high-throughput technique recently adopted is the use of molecular inversion probes (MIPs) to measure probe copy number changes. The resulting data consist of high-dimensional copy number profiles that can be used to ascertain probe-specific copy number alterations in correlative studies with patient outcomes to guide risk stratification and future treatment. We propose a novel Bayesian variable selection method, the hierarchical structured variable selection (HSVS) method, which accounts for the natural gene and probe-within-gene architecture to identify important genes and probes associated with clinically relevant outcomes. We propose the HSVS model for grouped variable selection, where simultaneous selection of both groups and within-group variables is of interest. The HSVS model utilizes a discrete mixture prior distribution for group selection and group-specific Bayesian lasso hierarchies for variable selection within groups. We provide methods for accounting for serial correlations within groups that incorporate Bayesian fused lasso methods for within-group selection. Through simulations we establish that our method results in lower model errors than other methods when a natural grouping structure exists. We apply our method to an MIP study of breast cancer and show that it identifies genes and probes that are significantly associated with clinically relevant subtypes of breast cancer.
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