A Bayesian Graphical Model for ChIP-Seq Data on Histone Modifications
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Publication:4916928
DOI10.1080/01621459.2012.746058zbMath1379.62079OpenAlexW2052131982MaRDI QIDQ4916928
Shoudan Liang, Lu Yue, Riten Mitra, Yuan Ji, Peter Mueller
Publication date: 26 April 2013
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/01621459.2012.746058
Markov chain Monte Carlonetwork modelMarkov random fieldsautologistic regressionepigeneticspathway dependence
Related Items (8)
Modeling Network Populations via Graph Distances ⋮ Nonparametric Bayesian learning of heterogeneous dynamic transcription factor networks ⋮ Reciprocal graphical models for integrative gene regulatory network analysis ⋮ Bayesian state space models for dynamic genetic network construction across multiple tissues ⋮ Bayesian graphical models for differential pathways ⋮ Propriety conditions for the Bayesian autologistic model-inference for histone modifications ⋮ Phylogenetically informed Bayesian truncated copula graphical models for microbial association networks ⋮ Bayesian nonparametric clustering for large data sets
Uses Software
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- A Statistical Framework for the Analysis of ChIP-Seq Data
- Learning Causal Bayesian Network Structures From Experimental Data
- A Statistical Framework for Expression-Based Molecular Classification in Cancer
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