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Reverse-engineering transcriptional modules from gene expression data

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Abstract
"Module networks" are a framework to learn gene regulatory networks from expression data using a probabilistic model in which coregulated genes share the same parameters and conditional distributions. We present a method to infer ensembles of such networks and an averaging procedure to extract the statistically most significant modules and their regulators. We show that the inferred probabilistic models extend beyond the dataset used to learn the models.
Keywords
GADX, RESPONSIVE REGULATORY PROTEIN, ensemble methods, probabilistic graphical models, transcriptional modules, MESSENGER-RNA EXPRESSION, reverse engineering, ESCHERICHIA-COLI, NETWORKS, LRP, ORGANIZATION, BINDING, PROFILES

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Chicago
Michoel, Tom, Riet De Smet, Anagha Madhusudan Joshi, Kathleen Marchal, and Yves Van de Peer. 2009. “Reverse-engineering Transcriptional Modules from Gene Expression Data.” Ed. Gustavo Stolovitzky, Pascal Kahlem, and Andrea Califano. Annals of the New York Academy of Sciences 1158: 36–43.
APA
Michoel, T., De Smet, R., Joshi, A. M., Marchal, K., & Van de Peer, Y. (2009). Reverse-engineering transcriptional modules from gene expression data. (Gustavo Stolovitzky, P. Kahlem, & A. Califano, Eds.)Annals of the New York Academy of Sciences, 1158, 36–43. Presented at the ENFIN-DREAM Conference on the Assessment of Computational Methods in Systems Biology (DREAM2 Conference).
Vancouver
1.
Michoel T, De Smet R, Joshi AM, Marchal K, Van de Peer Y. Reverse-engineering transcriptional modules from gene expression data. Stolovitzky G, Kahlem P, Califano A, editors. Annals of the New York Academy of Sciences. Malden, MA, USA: Wiley-Blackwell; 2009;1158:36–43.
MLA
Michoel, Tom, Riet De Smet, Anagha Madhusudan Joshi, et al. “Reverse-engineering Transcriptional Modules from Gene Expression Data.” Ed. Gustavo Stolovitzky, Pascal Kahlem, & Andrea Califano. Annals of the New York Academy of Sciences 1158 (2009): 36–43. Print.
@article{672746,
  abstract     = {{\textacutedbl}Module networks{\textacutedbl} are a framework to learn gene regulatory networks from expression data using a probabilistic model in which coregulated genes share the same parameters and conditional distributions. We present a method to infer ensembles of such networks and an averaging procedure to extract the statistically most significant modules and their regulators. We show that the inferred probabilistic models extend beyond the dataset used to learn the models.},
  author       = {Michoel, Tom and De Smet, Riet and Joshi, Anagha Madhusudan and Marchal, Kathleen and Van de Peer, Yves},
  editor       = {Stolovitzky, Gustavo and Kahlem, Pascal and Califano, Andrea},
  isbn         = {9781573317511},
  issn         = {0077-8923},
  journal      = {Annals of the New York Academy of Sciences},
  keyword      = {GADX,RESPONSIVE REGULATORY PROTEIN,ensemble methods,probabilistic graphical models,transcriptional modules,MESSENGER-RNA EXPRESSION,reverse engineering,ESCHERICHIA-COLI,NETWORKS,LRP,ORGANIZATION,BINDING,PROFILES},
  language     = {eng},
  location     = {Madrid, Spain},
  pages        = {36--43},
  publisher    = {Wiley-Blackwell},
  title        = {Reverse-engineering transcriptional modules from gene expression data},
  url          = {http://dx.doi.org/10.1111/j.1749-6632.2008.03943.x},
  volume       = {1158},
  year         = {2009},
}

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