A posterior probability approach for gene regulatory network inference in genetic perturbation data

DOI10.3934/MBE.2016041zbMath1388.62338arXiv1603.04835OpenAlexW2302531807WikidataQ31138678 ScholiaQ31138678MaRDI QIDQ326558

Ka Yee Yeung, William Chad Young, Adrian E. Raftery

Publication date: 12 October 2016

Published in: Mathematical Biosciences and Engineering (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1603.04835

zbMATH Keywords

Bayesian analysisstatisticsgene regulatory networkstatistical computation

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Genetics and epigenetics (92D10) Systems biology, networks (92C42)

Related Items (2)

A genetic regulatory network based method for multi-objective sequencing problem in mixed-model assembly lines ⋮ Identifying dynamical time series model parameters from equilibrium samples, with application to gene regulatory networks

Uses Software

• bnlearn
• minet
• Enrichr
• TRANSWESD
• ARACNE
• TRANSFAC
• JASPAR
• CGBayesNets

Cites Work

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• Bayesian model averaging: A tutorial. (with comments and a rejoinder).
• Model uncertainty
• Minimax risks for sparse regressions: ultra-high dimensional phenomenons
• Autoregressive models for gene regulatory network inference: sparsity, stability and causality issues
• Significance analysis of microarrays applied to the ionizing radiation response
• Bayesian Model Averaging for Linear Regression Models
• Bayes Factors
• Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$-Constrained Quadratic Programming (Lasso)

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