SIMoNe

simone: Statistical Inference for MOdular NEtworks (SIMoNe) , The R package simone implements the inference of co-expression networks based on partial correlation coefficients from either steady-state or time-course transcriptomic data. Note that with both type of data this package can deal with samples collected in different experimental conditions and therefore not identically distributed. In this particular case, multiple but related networks are inferred on one simone run. (Source: http://cran.r-project.org/web/packages)

Keywords for this software

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References in zbMATH (referenced in 6 articles , 1 standard article )

Showing results 1 to 6 of 6.
Sorted by year (citations)

  1. Sanguinetti, Guido (ed.); Huynh-Thu, Vân Anh (ed.): Gene regulatory networks. Methods and protocols (2019)
  2. Viallon, Vivian; Lambert-Lacroix, Sophie; Hoefling, Hölger; Picard, Franck: On the robustness of the generalized fused Lasso to prior specifications (2016)
  3. Nagarajan, Radhakrishnan; Scutari, Marco; Lèbre, Sophie: Bayesian networks in R. With applications in systems biology (2013)
  4. Chiquet, Julien; Grandvalet, Yves; Ambroise, Christophe: Inferring multiple graphical structures (2011)
  5. Ambroise, Christophe; Chiquet, Julien; Matias, Catherine: Inferring sparse Gaussian graphical models with latent structure (2009)
  6. Chiquet, Julien; Smith, Alexander; Grasseau, Gilles; Matias, Catherine; Ambroise, Christophe: Simone: statistical inference for modular networks (2009) ioport


Further publications can be found at: http://stat.genopole.cnrs.fr/publications/publications