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

2.50
Hdl Handle:
http://hdl.handle.net/10033/14201
Title:
SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms.
Authors:
Van den Bulcke, Tim; Van Leemput, Koenraad; Naudts, Bart; van Remortel, Piet; Ma, Hongwu; Verschoren, Alain; De Moor, Bart; Marchal, Kathleen
Abstract:
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. RESULTS: In 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. CONCLUSION: This 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.
Citation:
BMC Bioinformatics 2006, 7:43
Issue Date:
2006
URI:
http://hdl.handle.net/10033/14201
DOI:
10.1186/1471-2105-7-43
PubMed ID:
16438721
Type:
Article
Language:
en
ISSN:
1471-2105
Appears in Collections:
Publications of Dept. Genome Analysis (GNA)

Full metadata record

DC FieldValue Language
dc.contributor.authorVan den Bulcke, Tim-
dc.contributor.authorVan Leemput, Koenraad-
dc.contributor.authorNaudts, Bart-
dc.contributor.authorvan Remortel, Piet-
dc.contributor.authorMa, Hongwu-
dc.contributor.authorVerschoren, Alain-
dc.contributor.authorDe Moor, Bart-
dc.contributor.authorMarchal, Kathleen-
dc.date.accessioned2007-10-23T08:19:09Z-
dc.date.available2007-10-23T08:19:09Z-
dc.date.issued2006-
dc.identifier.citationBMC Bioinformatics 2006, 7:43en
dc.identifier.issn1471-2105-
dc.identifier.pmid16438721-
dc.identifier.doi10.1186/1471-2105-7-43-
dc.identifier.urihttp://hdl.handle.net/10033/14201-
dc.description.abstractBACKGROUND: 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. RESULTS: In 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. CONCLUSION: This 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.en
dc.format.extent822547 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoenen
dc.titleSynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms.en
dc.typeArticleen
dc.format.digYES-

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