This article is part of the supplement: Probabilistic Modeling and Machine Learning in Structural and Systems Biology

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Validating module network learning algorithms using simulated data

Tom Michoel1*, Steven Maere1, Eric Bonnet1, Anagha Joshi1, Yvan Saeys1, Tim Van den Bulcke2, Koenraad Van Leemput3, Piet van Remortel3, Martin Kuiper1, Kathleen Marchal24 and Yves Van de Peer1

Author Affiliations

1 Bioinformatics & Evolutionary Genomics, Department of Plant Systems Biology, VIB/Ghent University, Technologiepark 927, B-9052 Ghent, Belgium

2 ESAT-SCD, K.U.Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium

3 ISLab, Department of Mathematics and Computer Science, University of Antwerp, Middelheimlaan 1, B-2020 Antwerpen, Belgium

4 CMPG, Department Microbial and Molecular Systems, K.U.Leuven, Kasteelpark Arenberg 20, B-3001 Leuven, Belgium

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BMC Bioinformatics 2007, 8(Suppl 2):S5  doi:10.1186/1471-2105-8-S2-S5

Published: 3 May 2007



In recent years, several authors have used probabilistic graphical models to learn expression modules and their regulatory programs from gene expression data. Despite the demonstrated success of such algorithms in uncovering biologically relevant regulatory relations, further developments in the area are hampered by a lack of tools to compare the performance of alternative module network learning strategies. Here, we demonstrate the use of the synthetic data generator SynTReN for the purpose of testing and comparing module network learning algorithms. We introduce a software package for learning module networks, called LeMoNe, which incorporates a novel strategy for learning regulatory programs. Novelties include the use of a bottom-up Bayesian hierarchical clustering to construct the regulatory programs, and the use of a conditional entropy measure to assign regulators to the regulation program nodes. Using SynTReN data, we test the performance of LeMoNe in a completely controlled situation and assess the effect of the methodological changes we made with respect to an existing software package, namely Genomica. Additionally, we assess the effect of various parameters, such as the size of the data set and the amount of noise, on the inference performance.


Overall, application of Genomica and LeMoNe to simulated data sets gave comparable results. However, LeMoNe offers some advantages, one of them being that the learning process is considerably faster for larger data sets. Additionally, we show that the location of the regulators in the LeMoNe regulation programs and their conditional entropy may be used to prioritize regulators for functional validation, and that the combination of the bottom-up clustering strategy with the conditional entropy-based assignment of regulators improves the handling of missing or hidden regulators.


We show that data simulators such as SynTReN are very well suited for the purpose of developing, testing and improving module network algorithms. We used SynTReN data to develop and test an alternative module network learning strategy, which is incorporated in the software package LeMoNe, and we provide evidence that this alternative strategy has several advantages with respect to existing methods.