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SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms |
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| Authors: | Tim Van den Bulcke Koenraad Van Leemput Bart Naudts Piet van Remortel Hongwu Ma Alain Verschoren Bart De Moor and Kathleen Marchal |
| Institution: | (1) ESAT-SCD, K.U.Leuven, Kasteelpark Arenberg 10, B-3001 Heverlee, Belgium;(2) ISLab, Dept. Math. and Comp. Sc., University of Antwerp, Middelheimlaan 1, B-2020 Antwerpen, Belgium;(3) Dept. of Genome Analysis, German Research Center for Biotechnology, Mascheroder Weg 1, D-38124 Braunschweig, Germany;(4) CMPG, Dept. Microbial and Molecular Systems, K.U.Leuven, Kasteelpark Arenberg 20, B-3001 Heverlee, Belgium |
| Abstract: | 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. |
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