Generating Realistic In Silico Gene Networks for Performance Assessment of Reverse Engineering Methods

By Daniel Marbach, Thomas Schaffter, Claudio Mattiussi, Dario Floreano. J. Comput. Biol., 2009

491 citations recorded by the source corpus.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/98bdc2ae2546cf76c742f13daab3f3e17ef4e46c

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Citations in the corpus

Inferring Regulatory Networks from Expression Data Using Tree-Based Methods (2010, methodology): “All networks and data were generated with GeneNetWeaver (GNW) version 2.0 [38].”
Towards a Rigorous Assessment of Systems Biology Models: The DREAM3 Challenges (2010, methodology): “Researchers have used a variety of algorithms to deduce the structure of very different biological and artificial networks [1] and have evaluated their success using various metrics [2–8].”
D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery (2021, methodology): “DYNOTEARS is evaluated on S&P 500 returns data with 97 vertices, and on DREAM4 with 100 vertices [157].”
Estimating the effect of joint interventions from observational data in sparse high-dimensional settings (2014, methodology): “ 17 of [21], we compare the performance of the joint-IDA estimators based on RRC, MCD or IPW in simulation studies with high dimensional settings. 7 Application The DREAM4 In Silico Network Challenge [18] is a competition in reverse engineering of gene regulation networks, involving ve networks of s”
Revealing strengths and weaknesses of methods for gene network inference (2010, methodology): “We produced realistic network structures by extracting modules from known biological interaction networks (19).”
Network Reconstruction From High-Dimensional Ordinary Differential Equations (2016, methodology): “GeneNetWeaver (GNW) provides an in silico benchmark for assessing the performance of network recovery methods (Schaffter et al., 2011), and was used in the third DREAM challenge (Marbach et al., 2009).”
Characterization and greedy learning of interventional Markov equivalence classes of directed acyclic graphs (2011, background): “The DREAM4 challenge provides ﬁve data sets with an ensemble of interventional and observational data simulated from ﬁve biologically plausible, possibly cyclic gene regulatory networks with 10 genes (Marbach et al., 2009).”
Network Inference and Biological Dynamics (2011, methodology): “Assessment methodology has received attention, including attempts to automate the generation of large scale biological network models for automatic benchmarking of performance [Marbach et al. (2009); Van den Bulcke et al. (2006)].”
Wisdom of crowds for robust gene network inference (2012, methodology): “The fourth dataset is an in silico counterpart to the E. coli dataset, generated using GeneNetWeaver 52,53 (version 4.0).”
Learning linear cyclic causal models with latent variables (2012, methodology): “The data generating models were designed to be biologically plausible (Marbach et al., 2009) in order to achieve a realistic performance assessment of the network learning algorithms.”
Learning Causal Structures Using Regression Invariance (2017, methodology): “We considered GRNs in “DREAM 3 In Silico Network" challenge, conducted in 2008 [19].”