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A Semiparametric Bayesian Method of Clustering Genes Using Time-Series of Expression Profiles

  • Arvind K. Jammalamadaka
  • Kaushik Ghosh
Chapter

Abstract

An increasing number of microarray experiments look at expression levels of genes over the course of several points in time. In this article, we present two models for clustering such time series of expression profiles. We use nonparametric Bayesian methods which make the models robust to misspecifications and provide a natural framework for clustering of the genes through the use of Dirichlet process priors. Unlike other clustering techniques, the resulting number of clusters is completely data driven. We demonstrate the effectiveness of our methodology using simulation studies with artificial data as well as through an application to a real data set.

Keywords

Dirichlet Process Heteroscedastic Model Markov Chain Monte Carlo Procedure Dirichlet Process Mixture Model Time Series Gene Expression 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Computer Science and Artificial Intelligence LaboratoryMassachusetts Institute of TechnologyCambridgeUSA

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