Gene regulatory networks from multifactorial perturbations using graphical lasso: Application to the DREAM4 challenge

Menendez, Patricia, Kourmpetis, Yiannis A.I., ter Braak, Cajo J.F. and van Eeuwijk, Fred A. (2010) Gene regulatory networks from multifactorial perturbations using graphical lasso: Application to the DREAM4 challenge. PLoS One, 5 12: 14147.1-14147.8. doi:10.1371/journal.pone.0014147


Author Menendez, Patricia
Kourmpetis, Yiannis A.I.
ter Braak, Cajo J.F.
van Eeuwijk, Fred A.
Title Gene regulatory networks from multifactorial perturbations using graphical lasso: Application to the DREAM4 challenge
Journal name PLoS One   Check publisher's open access policy
ISSN 1932-6203
Publication date 2010-12-20
Year available 2010
Sub-type Article (original research)
DOI 10.1371/journal.pone.0014147
Open Access Status DOI
Volume 5
Issue 12
Start page 14147.1
End page 14147.8
Total pages 8
Place of publication San Francisco, CA United States
Publisher Public Library of Science
Collection year 2011
Language eng
Formatted abstract

A major challenge in the field of systems biology consists of predicting gene regulatory networks based on different training data. Within the DREAM4 initiative, we took part in the multifactorial sub-challenge that aimed to predict gene regulatory networks of size 100 from training data consisting of steady-state levels obtained after applying multifactorial perturbations to the original in silico network.

Due to the static character of the challenge data, we tackled the problem via a sparse Gaussian Markov Random Field, which relates network topology with the covariance inverse generated by the gene measurements. As for the computations, we used the Graphical Lasso algorithm which provided a large range of candidate network topologies. The main task was to select the optimal network topology and for that, different model selection criteria were explored. The selected networks were compared with the golden standards and the results ranked using the scoring metrics applied in the challenge, giving a better insight in our submission and the way to improve it.

Our approach provides an easy statistical and computational framework to infer gene regulatory networks that is suitable for large networks, even if the number of the observations (perturbations) is greater than the number of variables (genes).

Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Mathematics and Physics
 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 16 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 24 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Fri, 18 Jan 2013, 08:58:16 EST by Patricia Menendez Galvan on behalf of Mathematics