SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms. Background: The development of algorithms to infer the structure of gene regulatory networks based on expression data is an important subject in bioinformatics research. Validation of these algorithms requires benchmark data sets for which the underlying network is known. Since experimental data sets of the appropriate size and design are usually not available, there is a clear need to generate well-characterized synthetic data sets that allow thorough testing of learning algorithms in a fast and reproducible manner.

References in zbMATH (referenced in 17 articles )

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  1. Guidotti, Riccardo: Evaluating local explanation methods on ground truth (2021)
  2. Tyler Grimes, Somnath Datta: SeqNet: An R Package for Generating Gene-Gene Networks and Simulating RNA-Seq Data (2021) not zbMATH
  3. Pio, Gianvito; Ceci, Michelangelo; Prisciandaro, Francesca; Malerba, Donato: Exploiting causality in gene network reconstruction based on graph embedding (2020)
  4. Karaca, Yeliz; Cattani, Carlo: Computational methods for data analysis (2019)
  5. Sanguinetti, Guido (ed.); Huynh-Thu, Vân Anh (ed.): Gene regulatory networks. Methods and protocols (2019)
  6. Romano, Simone; Vinh, Nguyen Xuan; Verspoor, Karin; Bailey, James: The randomized information coefficient: assessing dependencies in noisy data (2018)
  7. Djordjilović, Vera; Chiogna, Monica; Vomlel, Jiří: An empirical comparison of popular structure learning algorithms with a view to gene network inference (2017)
  8. Keith, Jonathan M. (ed.): Bioinformatics. Volume II: structure, function, and applications (2017)
  9. 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)
  10. Miecznikowski, Jeffrey C.; Gaile, Daniel P.; Chen, Xiwei; Tritchler, David L.: Identification of consistent functional genetic modules (2016)
  11. Liu, Song; Quinn, John A.; Gutmann, Michael U.; Suzuki, Taiji; Sugiyama, Masashi: Direct learning of sparse changes in Markov networks by density ratio estimation (2014)
  12. Nikolova, Olga; Zola, Jaroslaw; Aluru, Srinivas: Parallel globally optimal structure learning of Bayesian networks (2013)
  13. Oates, Chris J.; Mukherjee, Sach: Network inference and biological dynamics (2012)
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  15. Anvar, S. Yahya; Hoen, Peter A. C.’t; Tucker, Allan: The identification of informative genes from multiple datasets with increasing complexity (2010) ioport
  16. Liew, Alan Wee-Chung; Law, Ngai-Fong; Cao, Xiao-Qin; Yan, Hong: Statistical power of Fisher test for the detection of short periodic gene expression profiles (2009)
  17. Tritchler, David; Parkhomenko, Elena; Beyene, Joseph: Filtering genes for cluster and network analysis (2009) ioport