Using higher-order dynamic bayesian networks to model periodic data from the circadian clock of Arabidopsis Thaliana

Daly, R. , Edwards, K.D., O'Neill, J.S., Aitken, S., Millar, A.J. and Girolami, M. (2009) Using higher-order dynamic bayesian networks to model periodic data from the circadian clock of Arabidopsis Thaliana. In: Kadirkamanathan, V., Sanguinetti, G. and Noirel, J. (eds.) Pattern Recognition in Bioinformatics: 4th IAPR International Conference, PRIB 2009, Sheffield, UK, September 7-9, 2009. Proceedings. Series: Lecture notes in computer science (5780). Springer, pp. 67-78. ISBN 9783642040306 (doi: 10.1007/978-3-642-04031-3_7)

Abstract

Modelling gene regulatory networks in organisms is an important task that has recently become possible due to large scale assays using technologies such as microarrays. In this paper, the circadian clock of Arabidopsis thaliana is modelled by fitting dynamic Bayesian networks to luminescence data gathered from experiments. This work differs from previous modelling attempts by using higher-order dynamic Bayesian networks to explicitly model the time lag between the various genes being expressed. In order to achieve this goal, new techniques in preprocessing the data and in evaluating a learned model are proposed. It is shown that it is possible, to some extent, to model these time delays using a higher-order dynamic Bayesian network.

• Item Type: Book Sections
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Girolami, Prof Mark and Daly, Dr Ronan
• Authors: Daly, R., Edwards, K.D., O'Neill, J.S., Aitken, S., Millar, A.J., and Girolami, M.
• Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
• College/School: College of Science and Engineering > School of Computing Science
• Publisher: Springer
• ISBN: 9783642040306

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