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Module networks revisited: computational assessment and prioritization of model predictions

Anagha Madhusudan Joshi UGent, Riet De Smet UGent, Kathleen Marchal UGent, Yves Van de Peer UGent and Tom Michoel UGent (2009) BIOINFORMATICS. 25(4). p.490-496
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.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
GENOME, BIOLOGY, PROFILES, REGULATORY NETWORKS, MOUSE, GENE-EXPRESSION DATA
journal title
BIOINFORMATICS
Bioinformatics
volume
25
issue
4
pages
490 - 496
Web of Science type
Article
Web of Science id
000263406000011
JCR category
MATHEMATICAL & COMPUTATIONAL BIOLOGY
JCR impact factor
4.926 (2009)
JCR rank
2/29 (2009)
JCR quartile
1 (2009)
ISSN
1367-4803
DOI
10.1093/bioinformatics/btn658
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
528407
handle
http://hdl.handle.net/1854/LU-528407
date created
2009-03-23 16:10:11
date last changed
2013-09-16 15:25:28
@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},
}

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.