GeneNetWeaver (GNW): GNW is an open-source tool for in silico benchmark generation and performance profiling of network inference methods. We are using in vivo microarray compendia side-by-side with synthetic (GNW) data to assess the performance of network inference methods in the DREAM challenge. You can launch GNW directly from your browser by clicking the button in the sidebar. If it doesn’t work, make sure that you have Java web start installed. GNW is free software, released under an MIT license. The source code and additional resources are available at

References in zbMATH (referenced in 13 articles )

Showing results 1 to 13 of 13.
Sorted by year (citations)

  1. Tyler Grimes, Somnath Datta: SeqNet: An R Package for Generating Gene-Gene Networks and Simulating RNA-Seq Data (2021) not zbMATH
  2. S. Thomas Kelly; Michael A. Black: graphsim: An R package for simulating gene expression data from graph structures of biological pathways (2020) not zbMATH
  3. Jensen, David: Comment: strengthening empirical evaluation of causal inference methods (2019)
  4. Sanguinetti, Guido (ed.); Huynh-Thu, Vân Anh (ed.): Gene regulatory networks. Methods and protocols (2019)
  5. Young, William Chad; Yeung, Ka Yee; Raftery, Adrian E.: Identifying dynamical time series model parameters from equilibrium samples, with application to gene regulatory networks (2019)
  6. Khan, Abhinandan; Saha, Goutam; Pal, Rajat Kumar: An approach for reduction of false predictions in reverse engineering of gene regulatory networks (2018)
  7. Ben Abdallah, Emna; Ribeiro, Tony; Magnin, Morgan; Roux, Olivier; Inoue, Katsumi: Modeling delayed dynamics in biological regulatory networks from time series data (2017)
  8. Weishaupt, Holger; Johansson, Patrik; Engström, Christopher; Nelander, Sven; Silvestrov, Sergei; Swartling, Fredrik J.: Loss of conservation of graph centralities in reverse-engineered transcriptional regulatory networks (2017)
  9. Fioretto, Ferdinando; Dovier, Agostino; Pontelli, Enrico: Constrained community-based gene regulatory network inference (2015)
  10. Lim, Néhémy; d’Alché-Buc, Florence; Auliac, Cédric; Michailidis, George: Operator-valued kernel-based vector autoregressive models for network inference (2015)
  11. Statnikov, Alexander; Ma, Sisi; Henaff, Mikael; Lytkin, Nikita; Efstathiadis, Efstratios; Peskin, Eric R.; Aliferis, Constantin F.: Ultra-scalable and efficient methods for hybrid observational and experimental local causal pathway discovery (2015)
  12. Champion, Magali; Cierco-Ayrolles, Christine; Gadat, Sébastien; Vignes, Matthieu: Sparse regression and support recovery with (\mathbbL_2)-boosting algorithms (2014)
  13. Meister, Arwen; Li, Ye Henry; Choi, Bokyung; Wong, Wing Hung: Learning a nonlinear dynamical system model of gene regulation: A perturbed steady-state approach (2013)

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