Sparse inverse covariance estimation for high-throughput microRNA sequencing data in the Poisson log-normal graphical model

DOI10.1080/00949655.2019.1657116OpenAlexW2969851703WikidataQ127337082 ScholiaQ127337082MaRDI QIDQ5107510

David Sinclair, Giles Hooker

Publication date: 27 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1708.04490

zbMATH Keywords

EM algorithmgraphical LassomiRNAPoisson network

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Image analysis in multivariate analysis (62H35)

Uses Software

• R
• S-PLUS
• glasso
• huge
• XMRF

Cites Work

• Sparse inverse covariance estimation with the graphical lasso
• A Bayesian graphical modeling approach to microRNA regulatory network inference
• High-dimensional Ising model selection using \(\ell _{1}\)-regularized logistic regression
• High-dimensional graphs and variable selection with the Lasso
• Statistical mechanics of complex networks
• Markov Random Fields and Their Applications
• The multivariate Poisson-log normal distribution
• Sparse estimation of multivariate Poisson log‐normal models from count data
• The huge Package for High-dimensional Undirected Graph Estimation in R
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