Incorporating biological information into linear models: a Bayesian approach to the selection of pathways and genes

DOI10.1214/11-AOAS463zbMath1228.62150arXiv1111.5419OpenAlexW3101256146WikidataQ40614440 ScholiaQ40614440MaRDI QIDQ652363

Francesco C. Stingo, Yian A. Chen, Marina Vannucci, Mahlet G. Tadesse

Publication date: 14 December 2011

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1111.5419

zbMATH Keywords

Markov chain Monte Carlogene expressionBayesian variable selectionMarkov random field prior

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Censored data models (62N01) Bayesian inference (62F15) Biochemistry, molecular biology (92C40) Numerical analysis or methods applied to Markov chains (65C40)

Related Items (27)

Bayesian variable selection and estimation for group Lasso ⋮ Bayesian semiparametric model for pathway-based analysis with zero-inflated clinical outcomes ⋮ A Bayesian integrative approach for multi‐platform genomic data: A kidney cancer case study ⋮ Bayesian group selection with non-local priors ⋮ miRNA–target gene regulatory networks: A Bayesian integrative approach to biomarker selection with application to kidney cancer ⋮ Bayesian graphical models for modern biological applications ⋮ A graph Laplacian prior for Bayesian variable selection and grouping ⋮ A Bayesian nonparametric mixture model for selecting genes and gene subnetworks ⋮ Two-Level Bayesian Interaction Analysis for Survival Data Incorporating Pathway Information ⋮ Bayesian Interaction Selection Model for Multimodal Neuroimaging Data Analysis ⋮ Incorporating grouping information in Bayesian variable selection with applications in genomics ⋮ A Bayesian group selection with compositional responses for analysis of radiologic tumor proportions and their genomic determinants ⋮ Subject-specific Dirichlet-multinomial regression for multi-district microbiota data analysis ⋮ Bayesian pathway selection ⋮ The Gibbs-plaid biclustering model ⋮ Incorporating biological information into linear models: a Bayesian approach to the selection of pathways and genes ⋮ Learning from a lot: Empirical Bayes for high‐dimensional model‐based prediction ⋮ Selecting massive variables using an iterated conditional modes/medians algorithm ⋮ Joint Bayesian Variable and DAG Selection Consistency for High-dimensional Regression Models with Network-structured Covariates ⋮ Multivariate Bayesian variable selection exploiting dependence structure among outcomes: Application to air pollution effects on DNA methylation ⋮ A Bayesian Approach for Estimating Dynamic Functional Network Connectivity in fMRI Data ⋮ Bayesian Approaches for Large Biological Networks ⋮ Bayesian indicator variable selection to incorporate hierarchical overlapping group structure in multi-omics applications ⋮ Bayesian network marker selection via the thresholded graph Laplacian Gaussian prior ⋮ Spatial Bayesian variable selection and grouping for high-dimensional scalar-on-image regression ⋮ A Bayesian extension of the hypergeometric test for functional enrichment analysis ⋮ An Integrative Bayesian Modeling Approach to Imaging Genetics

Uses Software

• Bioconductor
• KEGGgraph
• MetaCyc
• GenMAPP
• KEGG
• impute
• Reactome
• pi-MASS
• MAPPFinder

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Bayesian variable selection regression for genome-wide association studies and other large-scale problems
• Modeling dependent gene expression
• Incorporating biological information into linear models: a Bayesian approach to the selection of pathways and genes
• A hidden spatial-temporal Markov random field model for network-based analysis of time course gene expression data
• Penalized regression, standard errors, and Bayesian Lassos
• Bayesian Variable Selection in Multinomial Probit Models to Identify Molecular Signatures of Disease Stage
• Incorporating Predictor Network in Penalized Regression with Application to Microarray Data
• Averaged gene expressions for regression
• Multivariate Bayesian Variable Selection and Prediction
• Exact sampling with coupled Markov chains and applications to statistical mechanics
• Bayesian Variable Selection in Structured High-Dimensional Covariate Spaces With Applications in Genomics
• Bayesian Analysis of Binary and Polychotomous Response Data
• Regularization and Variable Selection Via the Elastic Net
• Model Selection and Estimation in Regression with Grouped Variables
• An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants
• Prediction by Supervised Principal Components

This page was built for publication: Incorporating biological information into linear models: a Bayesian approach to the selection of pathways and genes