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Reverse-engineering transcriptional modules from gene expression data

Tom Michoel UGent, Riet De Smet UGent, Anagha Madhusudan Joshi UGent, Kathleen Marchal UGent and Yves Van de Peer UGent (2009) Annals of the New York Academy of Sciences. 1158. p.36-43
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.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (proceedingsPaper)
publication status
published
subject
keyword
GADX, RESPONSIVE REGULATORY PROTEIN, ensemble methods, probabilistic graphical models, transcriptional modules, MESSENGER-RNA EXPRESSION, reverse engineering, ESCHERICHIA-COLI, NETWORKS, LRP, ORGANIZATION, BINDING, PROFILES
journal title
Annals of the New York Academy of Sciences
Ann. N.Y. Acad. Sci.
editor
Gustavo Stolovitzky, Pascal Kahlem and Andrea Califano
volume
1158
issue title
Challenges of systems biology : community efforts to harness biological complexity
pages
36 - 43
publisher
Wiley-Blackwell
place of publication
Malden, MA, USA
conference name
ENFIN-DREAM Conference on the Assessment of Computational Methods in Systems Biology (DREAM2 Conference)
conference location
Madrid, Spain
conference start
2008-04-28
conference end
2008-04-29
Web of Science type
Proceedings Paper
Web of Science id
000265650800004
JCR category
MULTIDISCIPLINARY SCIENCES
JCR impact factor
2.67 (2009)
JCR rank
5/48 (2009)
JCR quartile
1 (2009)
ISSN
0077-8923
ISBN
9781573317511
DOI
10.1111/j.1749-6632.2008.03943.x
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
672746
handle
http://hdl.handle.net/1854/LU-672746
date created
2009-05-29 17:29:55
date last changed
2013-09-16 15:24:02
@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},
}

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. (G. 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.