Gene regulatory networks from multifactorial perturbations using Graphical Lasso: application to the DREAM4 challenge.

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...

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Autores principales:	Patricia Menéndez, Yiannis A I Kourmpetis, Cajo J F ter Braak, Fred A van Eeuwijk
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/81e89769d93a4cd69082f5c12826b3c4
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spelling	oai:doaj.org-article:81e89769d93a4cd69082f5c12826b3c42021-11-18T07:01:28ZGene regulatory networks from multifactorial perturbations using Graphical Lasso: application to the DREAM4 challenge.1932-620310.1371/journal.pone.0014147https://doaj.org/article/81e89769d93a4cd69082f5c12826b3c42010-12-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21188141/?tool=EBIhttps://doaj.org/toc/1932-6203A 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).Patricia MenéndezPatricia MenéndezYiannis A I KourmpetisCajo J F ter BraakFred A van EeuwijkPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 5, Iss 12, p e14147 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Patricia Menéndez Patricia Menéndez Yiannis A I Kourmpetis Cajo J F ter Braak Fred A van Eeuwijk Gene regulatory networks from multifactorial perturbations using Graphical Lasso: application to the DREAM4 challenge.
description	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).
format	article
author	Patricia Menéndez Patricia Menéndez Yiannis A I Kourmpetis Cajo J F ter Braak Fred A van Eeuwijk
author_facet	Patricia Menéndez Patricia Menéndez Yiannis A I Kourmpetis Cajo J F ter Braak Fred A van Eeuwijk
author_sort	Patricia Menéndez
title	Gene regulatory networks from multifactorial perturbations using Graphical Lasso: application to the DREAM4 challenge.
title_short	Gene regulatory networks from multifactorial perturbations using Graphical Lasso: application to the DREAM4 challenge.
title_full	Gene regulatory networks from multifactorial perturbations using Graphical Lasso: application to the DREAM4 challenge.
title_fullStr	Gene regulatory networks from multifactorial perturbations using Graphical Lasso: application to the DREAM4 challenge.
title_full_unstemmed	Gene regulatory networks from multifactorial perturbations using Graphical Lasso: application to the DREAM4 challenge.
title_sort	gene regulatory networks from multifactorial perturbations using graphical lasso: application to the dream4 challenge.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/81e89769d93a4cd69082f5c12826b3c4
work_keys_str_mv	AT patriciamenendez generegulatorynetworksfrommultifactorialperturbationsusinggraphicallassoapplicationtothedream4challenge AT patriciamenendez generegulatorynetworksfrommultifactorialperturbationsusinggraphicallassoapplicationtothedream4challenge AT yiannisaikourmpetis generegulatorynetworksfrommultifactorialperturbationsusinggraphicallassoapplicationtothedream4challenge AT cajojfterbraak generegulatorynetworksfrommultifactorialperturbationsusinggraphicallassoapplicationtothedream4challenge AT fredavaneeuwijk generegulatorynetworksfrommultifactorialperturbationsusinggraphicallassoapplicationtothedream4challenge
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Gene regulatory networks from multifactorial perturbations using Graphical Lasso: application to the DREAM4 challenge.

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