Bayesian state space models for dynamic genetic network construction across multiple tissues
DOI10.1515/sagmb-2014-0055zbMath1344.92012OpenAlexW2471934570WikidataQ39655734 ScholiaQ39655734MaRDI QIDQ309414
Publication date: 7 September 2016
Published in: Statistical Applications in Genetics and Molecular Biology (Search for Journal in Brave)
Full work available at URL: https://www.degruyter.com/view/j/sagmb.2016.15.issue-4/sagmb-2014-0055/sagmb-2014-0055.xml?format=INT
affymetrix time course datacorticosteroid treatmentdynamic genetic networkhierarchical Bayesian approachmultivariate state space model
Applications of statistics to biology and medical sciences; meta analysis (62P10) General biostatistics (92B15) Biochemistry, molecular biology (92C40) Systems biology, networks (92C42)
Related Items (1)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Non-homogeneous dynamic Bayesian networks for continuous data
- Quantifying the multi-scale performance of network inference algorithms
- Robust Bayesian graphical modeling using Dirichlet \(t\)-distributions
- Bayesian forecasting and dynamic models
- The positive false discovery rate: A Bayesian interpretation and the \(q\)-value
- A simple greedy algorithm for finding functional relations: Efficient implementation and average case analysis
- Efficient Gaussian graphical model determination under \(G\)-Wishart prior distributions
- A Metropolis-Hastings based method for sampling from the \(G\)-Wishart distribution in Gaussian graphical models
- Computational intelligence in medical informatics
- Following a Moving Target—Monte Carlo Inference for Dynamic Bayesian Models
- A Non-Homogeneous Dynamic Bayesian Network with Sequentially Coupled Interaction Parameters for Applications in Systems and Synthetic Biology
- Objective Bayesian model selection in Gaussian graphical models
- Covariance decomposition in undirected Gaussian graphical models
- On Gibbs sampling for state space models
- Empirical Bayes Analysis of a Microarray Experiment
- Bayesian Models for Gene Expression With DNA Microarray Data
- Cluster analysis of gene expression dynamics
- Bayesian Measures of Model Complexity and Fit
- A Bayesian Graphical Model for ChIP-Seq Data on Histone Modifications
- Time Series Analysis of Non-Gaussian Observations Based on State Space Models from Both Classical and Bayesian Perspectives
- The Joint Graphical Lasso for Inverse Covariance Estimation Across Multiple Classes
- Bayesian State Space Models for Inferring and Predicting Temporal Gene Expression Profiles
- Bayesian Finite Markov Mixture Model for Temporal Multi‐Tissue Polygenic Patterns
- Bayesian Inference of Multiple Gaussian Graphical Models
- Applied Bayesian Modelling
- Simulation of hyper-inverse Wishart distributions in graphical models
- Bayesian predictive information criterion for the evaluation of hierarchical Bayesian and empirical Bayes models
- Comparing the continuous representation of time-series expression profiles to identify differentially expressed genes
- Bayesian Models for Categorical Data
- Hierarchical Bayesian Neural Network for Gene Expression Temporal Patterns
- The Deviance Information Criterion: 12 Years on
- Dynamic matrix-variate graphical models
This page was built for publication: Bayesian state space models for dynamic genetic network construction across multiple tissues
