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Module networks revisited: computational assessment and prioritization of model predictions

(2009) BIOINFORMATICS. 25(4). p.490-496
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Abstract
Motivation: The solution of high-dimensional inference and prediction problems in computational biology is almost always a compromise between mathematical theory and practical constraints, such as limited computational resources. As time progresses, computational power increases but well-established inference methods often remain locked in their initial suboptimal solution. Results: We revisit the approach of Segal et al. to infer regulatory modules and their condition-specific regulators from gene expression data. In contrast to their direct optimization-based solution, we use a more representative centroid-like solution extracted from an ensemble of possible statistical models to explain the data. The ensemble method automatically selects a subset of most informative genes and builds a quantitatively better model for them. Genes which cluster together in the majority of models produce functionally more coherent modules. Regulators which are consistently assigned to a module are more often supported by literature, but a single model always contains many regulator assignments not supported by the ensemble. Reliably detecting condition-specific or combinatorial regulation is particularly hard in a single optimum but can be achieved using ensemble averaging.
Keywords
GENOME, BIOLOGY, PROFILES, REGULATORY NETWORKS, MOUSE, GENE-EXPRESSION DATA

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Chicago
Joshi, Anagha Madhusudan, Riet De Smet, Kathleen Marchal, Yves Van de Peer, and Tom Michoel. 2009. “Module Networks Revisited: Computational Assessment and Prioritization of Model Predictions.” Bioinformatics 25 (4): 490–496.
APA
Joshi, A. M., De Smet, R., Marchal, K., Van de Peer, Y., & Michoel, T. (2009). Module networks revisited: computational assessment and prioritization of model predictions. BIOINFORMATICS, 25(4), 490–496.
Vancouver
1.
Joshi AM, De Smet R, Marchal K, Van de Peer Y, Michoel T. Module networks revisited: computational assessment and prioritization of model predictions. BIOINFORMATICS. 2009;25(4):490–6.
MLA
Joshi, Anagha Madhusudan, Riet De Smet, Kathleen Marchal, et al. “Module Networks Revisited: Computational Assessment and Prioritization of Model Predictions.” BIOINFORMATICS 25.4 (2009): 490–496. Print.
@article{528407,
  abstract     = {Motivation: The solution of high-dimensional inference and prediction problems in computational biology is almost always a compromise between mathematical theory and practical constraints, such as limited computational resources. As time progresses, computational power increases but well-established inference methods often remain locked in their initial suboptimal solution. 
Results: We revisit the approach of Segal et al. to infer regulatory modules and their condition-specific regulators from gene expression data. In contrast to their direct optimization-based solution, we use a more representative centroid-like solution extracted from an ensemble of possible statistical models to explain the data. The ensemble method automatically selects a subset of most informative genes and builds a quantitatively better model for them. Genes which cluster together in the majority of models produce functionally more coherent modules. Regulators which are consistently assigned to a module are more often supported by literature, but a single model always contains many regulator assignments not supported by the ensemble. Reliably detecting condition-specific or combinatorial regulation is particularly hard in a single optimum but can be achieved using ensemble averaging.},
  author       = {Joshi, Anagha Madhusudan and De Smet, Riet and Marchal, Kathleen and Van de Peer, Yves and Michoel, Tom},
  issn         = {1367-4803},
  journal      = {BIOINFORMATICS},
  keyword      = {GENOME,BIOLOGY,PROFILES,REGULATORY NETWORKS,MOUSE,GENE-EXPRESSION DATA},
  language     = {eng},
  number       = {4},
  pages        = {490--496},
  title        = {Module networks revisited: computational assessment and prioritization of model predictions},
  url          = {http://dx.doi.org/10.1093/bioinformatics/btn658},
  volume       = {25},
  year         = {2009},
}

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