2007
DOI: 10.1186/1471-2105-8-s2-s5
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Validating module network learning algorithms using simulated data

Abstract: Background: 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 tes… Show more

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Cited by 39 publications
(42 citation statements)
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“…Also, generated top lists can be intersected to include or exclude certain genes. Finally, they can be grouped in compendiums to be analyzed by intricate clustering algorithms and to elaborate modules and networks (Michoel et al, 2007).…”
Section: Discussionmentioning
confidence: 99%
“…Also, generated top lists can be intersected to include or exclude certain genes. Finally, they can be grouped in compendiums to be analyzed by intricate clustering algorithms and to elaborate modules and networks (Michoel et al, 2007).…”
Section: Discussionmentioning
confidence: 99%
“…SynTReN is well suited for testing module network algorithms [13]. By using topologies generated based on previously described source networks, SynTReN allows good approximation of the statistical properties of real biological networks.…”
Section: Methodsmentioning
confidence: 99%
“…In order to anticipate these complexities in an intelligent way, we developed the LeMoNe (learning module networks) algorithm. 6,7 LeMoNe is a probabilistic module networks framework, like Genomica, 3 in which coregulated genes share the same parents and conditional distributions in a Bayesian network, hence limiting the number of variables, reducing the complexity of the learning and leading to more robust solutions. Using Gibbs sampling, LeMoNe groups genes in coexpression modules and then using a fuzzy decision tree, it assigns regulators to these gene modules, based on how well the regulators explain the condition-dependent expression behavior of the module.…”
Section: Introductionmentioning
confidence: 99%