Bayesian joint modeling of multiple gene networks and diverse genomic data to identify target genes of a transcription factor

DOI10.1214/11-AOAS502zbMath1235.62031arXiv1203.4359WikidataQ34191363 ScholiaQ34191363MaRDI QIDQ2428750

Peng Wei, Wei Pan

Publication date: 20 April 2012

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

Full work available at URL: https://arxiv.org/abs/1203.4359

zbMATH Keywords

Markov random fieldmixture modelssystems biologyBayesian hierarchical model

Mathematics Subject Classification ID

Random fields; image analysis (62M40) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Biochemistry, molecular biology (92C40) Numerical analysis or methods applied to Markov chains (65C40) Genetics and epigenetics (92D10) Systems biology, networks (92C42)

Related Items (3)

A Bayesian nonparametric mixture model for selecting genes and gene subnetworks ⋮ Bayesian joint modeling of multiple gene networks and diverse genomic data to identify target genes of a transcription factor ⋮ A Bayesian extension of the hypergeometric test for functional enrichment analysis

Uses Software

• WinBUGS
• KEGG
• RegulonDB

Cites Work

• Bayesian joint modeling of multiple gene networks and diverse genomic data to identify target genes of a transcription factor
• Bayesian variable selection and data integration for biological regulatory networks
• A hidden spatial-temporal Markov random field model for network-based analysis of time course gene expression data
• The most probable annotation problem in HMMs and its application to bioinformatics
• An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants
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