Reference : Elucidating the structure of genetic regulatory networks: a study of a second order d...
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/25730
Elucidating the structure of genetic regulatory networks: a study of a second order dynamical model on artificial data
English
Quach, Minh [University of Evry > IBISC FRE CNRS 2871 > > >]
Geurts, Pierre mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
d Alché-Buc, Florence [University of Evry > IBISC FRE CNRS 2871 > > >]
2006
Proc. of the 14th European Symposium on Artificial Neural Networks
Yes
No
International
14th European Symposium on Artificial Neural Networks
April 26-28, 2006
Bruges
Belgique
[en] bioinformatics ; machine learning
[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.
http://hdl.handle.net/2268/25730
http://www.montefiore.ulg.ac.be/services/stochastic/pubs/2006/QGD06

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