SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms

By T. V. D. Bulcke, K. V. Leemput, Bart Naudts, P. V. Remortel, Hongwu Ma, A. Verschoren, B. De Moor, Kathleen Marchal. BMC Bioinformatics, 2006

337 citations recorded by the source corpus.

BackgroundThe 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.ResultsIn this paper we describe a network generator that creates synthetic transcriptional regulatory networks and produces simulated gene expression data that approximates experimental data. Network topologies are generated by selecting subnetworks from previously described regulatory networks. Interaction kinetics are modeled by equations based on Michaelis-Menten and Hill kinetics. Our results show that the statistical properties of these topologies more closely approximate those of genuine biological networks than do those of different types of random graph models. Several user-definable parameters adjust the complexity of the resulting data set with respect to the structure learning algorithms.ConclusionThis network generation technique offers a valid alternative to existing methods. The topological characteristics of the generated networks more closely resemble the characteristics of real transcriptional networks. Simulation of the network scales well to large networks. The generator models different types of biological interactions and produces biologically plausible synthetic gene expression data.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/6826253edcc8ea6fb414f3f8aeff7828c2114aae

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Citations in the corpus

Generating Realistic In Silico Gene Networks for Performance Assessment of Reverse Engineering Methods (2009, positive): “However, random graphs capture only few of the structural properties of real biological gene networks (den Bulcke et al., 2006).”
Gradient-Based Neural DAG Learning (2019, positive): “We also consider pseudo-real data sets sampled from the SynTReN generator (Van den Bulcke, 2006).”
Synthetic Data for Deep Learning (2019, neutral): “[608] provide a generator for synthetic gene expression data, able to produce synthetic transcriptional regulatory networks and simulated gene expression data while closely matching real statistics of biological networks.”
Structural Agnostic Modeling: Adversarial Learning of Causal Graphs (2018, positive): “The SynTREN (Van den Bulcke et al., 2006) and GeneNetWeaver (GNW) (Schaffter et al., 2011) simulators of genetic regulatory networks have been used to generate observational data reflecting realistic complex regulatory mechanisms, high-order conditional dependencies between expression patterns and…”
Information-Theoretic Feature Selection in Microarray Data Using Variable Complementarity (2008, neutral): “As a result, when DISR is combined with a forward selection (as the criteria MRMR, CMIM and REL), is updated at each step by the variable , (37)”
Explainable and Interpretable Models in Computer Vision and Machine Learning (2017, positive): “Syntren artiﬁcial simulator First we apply CGNN on SynTREN (Van den Bulcke et al., 2006) from sub-networks of E. coli (Shen-Orr et al., 2002).”