ArticleJournal of the American Statistical Association2019
Bayesian Graphical Regression.
Article in Journal of the American Statistical Association, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed.
- SELF-Tree: An Interpretable Model for Multivariate Causal Direction Heterogeneity Analysis.Psychometrika · 2026Article
- Latent space-based network analysis for brain-behavior linking in neuroimaging.Nature methods · 2026Article
- Bayesian estimation of covariate assisted principal regression for brain functional connectivity.Biostatistics (Oxford, England) · 2025Article
- Connectivity Regression.Biostatistics (Oxford, England) · 2025Article
- Bayesian thresholded modeling for integrating brain node and network predictors.Biostatistics (Oxford, England) · 2025Article
- Beyond Euclid: an illustrated guide to modern machine learning with geometric, topological, and algebraic structures.Machine learning: science and technology · 2025Review
- Covariance-on-covariance regression.Biometrics · 2025Article
- A probabilistic modeling framework for genomic networks incorporating sample heterogeneity.Cell reports methods · 2025Article
- Multi-task Learning for Gaussian Graphical Regressions with High Dimensional Covariates.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2024Article
- A Double Regression Method for Graphical Modeling of High-dimensional Nonlinear and Non-Gaussian Data.Statistics and its interface · 2024Article
- Bayesian Spatial Blind Source Separation via the Thresholded Gaussian Process.Journal of the American Statistical Association · 2024Article
- Covariate-Assisted Bayesian Graph Learning for Heterogeneous Data.Journal of the American Statistical Association · 2024Article
- Federated Learning for Sparse Bayesian Models with Applications to Electronic Health Records and Genomics.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2023Article
- SpaceX: gene co-expression network estimation for spatial transcriptomics.Bioinformatics (Oxford, England) · 2022Article
- Feature selection and classification over the network with missing node observations.Statistics in medicine · 2022Article
- Bayesian Covariate-Dependent Gaussian Graphical Models with Varying Structure.Journal of machine learning research : JMLR · 2022Article
- Bayesian Edge Regression in Undirected Graphical Models to Characterize Interpatient Heterogeneity in Cancer.Journal of the American Statistical Association · 2022Article
- Bayesian graphical models for modern biological applications.Statistical methods & applications · 2022Review
- Bayesian Sparse Mediation Analysis with Targeted Penalization of Natural Indirect Effects.Journal of the Royal Statistical Society. Series C, Applied statistics · 2021Article
- Estimating Linear and Nonlinear Gene Coexpression Networks by Semiparametric Neighborhood Selection.Genetics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
We consider the problem of modeling conditional independence structures in heterogeneous data in the presence of additional subject-level covariates - termed Graphical Regression. We propose a novel specification of a conditional (in)dependence function of covariates - which allows the structure of a directed graph to vary flexibly with the covariates; imposes sparsity in both edge and covariate selection; produces both subject-specific and predictive graphs; and is computationally tractable. We provide theoretical justifications of our modeling endeavor, in terms of graphical model selection consistency. We demonstrate the performance of our method through rigorous simulation studies. We illustrate our approach in a cancer genomics-based precision medicine paradigm, where-in we explore gene regulatory networks in multiple myeloma taking prognostic clinical factors into account to obtain both population-level and subject-level gene regulatory networks.
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What OpenQuestion holds
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.