[en] Learning regulatory networks from time-series of gene expres-
sion is a challenging task. We propose to use synthetic data to analyze
the ability of a state-space model to retrieve the network structure while
varying a number of relevant problem parameters. ROC curves together
with new tools such as spectral clustering of local solutions found by EM
are used to analyze these results and provide relevant insights.
Disciplines :
Computer science
Author, co-author :
Quach, Minh; University of Evry > IBISC FRE CNRS 2871
Geurts, Pierre ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
d Alché-Buc, Florence; University of Evry > IBISC FRE CNRS 2871
Language :
English
Title :
Elucidating the structure of genetic regulatory networks: a study of a second order dynamical model on artificial data
Publication date :
2006
Event name :
14th European Symposium on Artificial Neural Networks
Event place :
Bruges, Belgium
Event date :
April 26-28, 2006
Audience :
International
Main work title :
Proc. of the 14th European Symposium on Artificial Neural Networks
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S. Kim, S. Imoto, and S. Miyano. Dynamic bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data. In Proc. of CMSB 2003, pages 104-113, 2003.
A. Regev I. Nachman and N. Friedman. Inferring quantitative models of regulatory networks from expression data. Bioinformatics, Vol. 20 Suppl. 1:i248-i256, 2004.
B-E. Perrin, L. Ralaivola, A. Mazurie, S. Bottani, J. Mallet, and F. D'Alché-Buc. Gene networks inference using dynamic bayesian networks. Bioinformatics, Vol. 19 Suppl. 2:i138-i148, 2003.
F. d'Alché Buc, P.-J. Lahaye, B.-E. Perrin, L. Ralaivola, T. Vujasinovic, A. Mazurie, and S. Bottani. Bioinformatics Using Computational Intelligence Paradigms, chapter A dynamical system based on inertia principle for gene regulatory network modeling, pages 93-118. Springer, 2005.
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