Bayesian network classifiers for time-series microarray data

Authors:
Allan Tucker;Veronica Vinciotti;Peter A. C. 't Hoen;Xiaohui Liu
Affiliations:
School of Information Systems Computing and Maths, Brunel University, Uxbridge, UK;School of Information Systems Computing and Maths, Brunel University, Uxbridge, UK;Center for Human and Clinical Genetics, Leiden Genome Technology Center, Leiden University Medical Center, Leiden, Netherlands;School of Information Systems Computing and Maths, Brunel University, Uxbridge, UK
Venue:
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Year:
2005

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Abstract

Microarray data from time-series experiments, where gene expression profiles are measured over the course of the experiment, require specialised algorithms. In this paper we introduce new architectures of Bayesian classifiers that highlight how both relative and absolute temporal relationships can be captured in order to understand how biological mechanisms differ. We show that these classifiers improve the classification of microarray data and at the same time ensure that the models can easily be analysed by biologists by incorporating time transparently. In this paper we focus on data that has been generated to explore different types of muscular dystrophy.