ArticleStatistics in medicine2016
Joint Bayesian variable and graph selection for regression models with network-structured predictors.
Article in Statistics in medicine, 2016. 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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed.
- BHMnet: Bayesian high-dimensional mediation analysis with network information integration for correlated mediators.Briefings in bioinformatics · 2026Article
- Bayesian Approach Integrating Prior Knowledge for Identifying miRNA-mRNA Interactions in Hepatocellular Carcinoma.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2023Article
- Regression-Based Bayesian Estimation and Structure Learning for Nonparanormal Graphical Models.Statistical analysis and data mining · 2022Article
- Spike-and-slab least absolute shrinkage and selection operator generalized additive models and scalable algorithms for high-dimensional data analysis.Statistics in medicine · 2022Article
- A Bayesian two-step integrative procedure incorporating prior knowledge for the identification of miRNA-mRNAs involved in hepatocellular carcinoma.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2022Article
- Bayesian regularization for a nonstationary Gaussian linear mixed effects model.Statistics in medicine · 2022Article
- Classification and prediction for multi-cancer data with ultrahigh-dimensional gene expressions.PloS one · 2022Article
- Bayesian graphical models for modern biological applications.Statistical methods & applications · 2022Review
- Gene-gene interaction analysis incorporating network information via a structured Bayesian approach.Statistics in medicine · 2021Article
- Combining heterogeneous subgroups with graph-structured variable selection priors for Cox regression.BMC bioinformatics · 2021Article
- 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
- Vertical integration methods for gene expression data analysis.Briefings in bioinformatics · 2021Review
- MicroBVS: Dirichlet-tree multinomial regression models with Bayesian variable selection - an R package.BMC bioinformatics · 2020Article
- Dynamic incorporation of prior knowledge from multiple domains in biomarker discovery.BMC bioinformatics · 2020Article
- Bayesian Network Marker Selection via the Thresholded Graph Laplacian Gaussian Prior.Bayesian analysis · 2020Article
- Hierarchical Normalized Completely Random Measures for Robust Graphical Modeling.Bayesian analysis · 2019Article
- An Expectation Conditional Maximization approach for Gaussian graphical models.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2019Article
- A Bayesian Approach for Learning Gene Networks Underlying Disease Severity in COPD.Statistics in biosciences · 2018Article
- The Application of Bayesian Methods in Cancer Prognosis and Prediction.Cancer genomics & proteomicsReview
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
In this work, we develop a Bayesian approach to perform selection of predictors that are linked within a network. We achieve this by combining a sparse regression model relating the predictors to a response variable with a graphical model describing conditional dependencies among the predictors. The proposed method is well-suited for genomic applications because it allows the identification of pathways of functionally related genes or proteins that impact an outcome of interest. In contrast to previous approaches for network-guided variable selection, we infer the network among predictors using a Gaussian graphical model and do not assume that network information is available a priori. We demonstrate that our method outperforms existing methods in identifying network-structured predictors in simulation settings and illustrate our proposed model with an application to inference of proteins relevant to glioblastoma survival.
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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.