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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationPattern Recognition in Bioinformatics
Subtitle of host publication4th IAPR International Conference, PRIB 2009, Sheffield, UK, September 7-9, 2009. Proceedings
PublisherSpringer-Verlag GmbH
Pages67-78
Number of pages12
ISBN (Print)978-3-642-04030-6
DOIs
Publication statusPublished - 2009

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin / Heidelberg
Volume5780
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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.

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