A Bayesian Approach for the Clustering of Short Time Series

Laurent Brehelin 1
1 MAB - Méthodes et Algorithmes pour la Bioinformatique
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Microarrays allow monitoring of thousands of genes over time periods. However, due to the low number of time points of the gene expression series, taking the temporal dependences into account when clustering the data is an hard task. Moreover, classes very interesting for the biologist, but sparse with regard to all the other genes, can be completely omitted by the standard approaches. We propose a Bayesian approach for this problem. A mixture model is used to describe and classify the data. The parameters of this model are constrained by a prior distribution defined with a new type of model that expresses our prior knowledge. These knowledge allow to take the temporal dependences into account in natural way, as well as to express rough temporal profiles about classes of interest.
Type de document :
Article dans une revue
Revue des Sciences et Technologies de l'Information - Série RIA : Revue d'Intelligence Artificielle, Lavoisier, 2006, 20, pp.697-716
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00113350
Contributeur : Laurent Brehelin <>
Soumis le : lundi 13 novembre 2006 - 10:34:02
Dernière modification le : jeudi 11 janvier 2018 - 06:26:12

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  • HAL Id : lirmm-00113350, version 1

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Laurent Brehelin. A Bayesian Approach for the Clustering of Short Time Series. Revue des Sciences et Technologies de l'Information - Série RIA : Revue d'Intelligence Artificielle, Lavoisier, 2006, 20, pp.697-716. 〈lirmm-00113350〉

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