ArticleBayesian analysis2020
Bayesian Network Marker Selection via the Thresholded Graph Laplacian Gaussian Prior.
Article in Bayesian analysis, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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Who cites it
8 citing papers in PubMed, 17 citations in OpenAlex.
- JOINT IDENTIFICATION OF SPATIALLY VARIABLE GENES VIA A NETWORK-ASSISTED BAYESIAN REGULARIZATION APPROACH.The annals of applied statistics · 2025Article
- Bayesian Spatial Blind Source Separation via the Thresholded Gaussian Process.Journal of the American Statistical Association · 2024Article
- Bayesian Covariate-Dependent Gaussian Graphical Models with Varying Structure.Journal of machine learning research : JMLR · 2022Article
- Bayesian Inferences on Neural Activity in EEG-Based Brain-Computer Interface.Journal of the American Statistical Association · 2022Article
- Gene-gene interaction analysis incorporating network information via a structured Bayesian approach.Statistics in medicine · 2021Article
- Bayesian hierarchical models for high-dimensional mediation analysis with coordinated selection of correlated mediators.Statistics in medicine · 2021Article
- Bayesian Sparse Mediation Analysis with Targeted Penalization of Natural Indirect Effects.Journal of the Royal Statistical Society. Series C, Applied statistics · 2021Article
- Bayesian Network Marker Selection via the Thresholded Graph Laplacian Gaussian Prior.Bayesian analysis · 2020Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Selecting informative nodes over large-scale networks becomes increasingly important in many research areas. Most existing methods focus on the local network structure and incur heavy computational costs for the large-scale problem. In this work, we propose a novel prior model for Bayesian network marker selection in the generalized linear model (GLM) framework: the Thresholded Graph Laplacian Gaussian (TGLG) prior, which adopts the graph Laplacian matrix to characterize the conditional dependence between neighboring markers accounting for the global network structure. Under mild conditions, we show the proposed model enjoys the posterior consistency with a diverging number of edges and nodes in the network. We also develop a Metropolis-adjusted Langevin algorithm (MALA) for efficient posterior computation, which is scalable to large-scale networks. We illustrate the superiorities of the proposed method compared with existing alternatives via extensive simulation studies and an analysis of the breast cancer gene expression dataset in the Cancer Genome Atlas (TCGA).
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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.