GeneNet

GeneNet: Modeling and Inferring Gene Networks. GeneNet is a package for analyzing gene expression (time series) data with focus on the inference of gene networks. In particular, GeneNet implements the methods of Schaefer and Strimmer (2005a,b,c) and Opgen-Rhein and Strimmer (2006, 2007) for learning large-scale gene association networks (including assignment of putative directions).

Keywords for this software

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References in zbMATH (referenced in 7 articles )

Showing results 1 to 7 of 7.
Sorted by year (citations)

  1. Sang, Peijun; Wang, Liangliang; Cao, Jiguo: Weighted empirical likelihood inference for dynamical correlations (2019)
  2. Lee, Namgil; Choi, Hyemi; Kim, Sung-Ho: Bayes shrinkage estimation for high-dimensional VAR models with scale mixture of normal distributions for noise (2016)
  3. Deng, Wanlu; Geng, Zhi; Li, Hongzhe: Learning local directed acyclic graphs based on multivariate time series data (2013)
  4. Müller, Hans-Georg; Yang, Wenjing: Dynamic relations for sparsely sampled Gaussian processes (2010)
  5. Krämer, Nicole; Schäfer, Juliane; Boulesteix, Anne-Laure: Regularized estimation of large-scale gene association networks using graphical Gaussian models (2009) ioport
  6. Telesca, Donatello; Inoue, Lurdes Y. T.; Neira, Mauricio; Etzioni, Ruth; Gleave, Martin; Nelson, Colleen: Differential expression and network inferences through functional data modeling (2009)
  7. Opgen-Rhein, Rainer; Strimmer, Korbinian: Inferring gene dependency networks from genomic longitudinal data: a functional data approach (2006)