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

Ronan Daly; Kieron D. Edwards; John S O'Neill; Stuart Aitken; Andrew Millar; Mark Girolami

doi:10.1007/978-3-642-04031-3_7

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

Ronan Daly, Kieron D. Edwards, John S O'Neill, Stuart Aitken, Andrew Millar, Mark Girolami

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	Pattern Recognition in Bioinformatics
Subtitle of host publication	4th IAPR International Conference, PRIB 2009, Sheffield, UK, September 7-9, 2009. Proceedings
Publisher	Springer
Pages	67-78
Number of pages	12
ISBN (Print)	978-3-642-04030-6
DOIs	https://doi.org/10.1007/978-3-642-04031-3_7
Publication status	Published - 2009

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer Berlin / Heidelberg
Volume	5780
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

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10.1007/978-3-642-04031-3_7

http://www.springerlink.com/content/t1515n8787174392

Cite this

Daly, R., Edwards, K. D., O'Neill, J. S., Aitken, S., Millar, A., & Girolami, M. (2009). Using higher-order dynamic Bayesian networks to model periodic data from the circadian clock of Arabidopsis Thaliana. In Pattern Recognition in Bioinformatics: 4th IAPR International Conference, PRIB 2009, Sheffield, UK, September 7-9, 2009. Proceedings (pp. 67-78). (Lecture Notes in Computer Science; Vol. 5780). Springer. https://doi.org/10.1007/978-3-642-04031-3_7

Daly, Ronan ; Edwards, Kieron D. ; O'Neill, John S et al. / Using higher-order dynamic Bayesian networks to model periodic data from the circadian clock of Arabidopsis Thaliana. Pattern Recognition in Bioinformatics: 4th IAPR International Conference, PRIB 2009, Sheffield, UK, September 7-9, 2009. Proceedings. Springer, 2009. pp. 67-78 (Lecture Notes in Computer Science).

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

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.",

author = "Ronan Daly and Edwards, {Kieron D.} and O'Neill, {John S} and Stuart Aitken and Andrew Millar and Mark Girolami",

year = "2009",

doi = "10.1007/978-3-642-04031-3_7",

language = "English",

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Daly, R, Edwards, KD, O'Neill, JS, Aitken, S , Millar, A & Girolami, M 2009, Using higher-order dynamic Bayesian networks to model periodic data from the circadian clock of Arabidopsis Thaliana. in Pattern Recognition in Bioinformatics: 4th IAPR International Conference, PRIB 2009, Sheffield, UK, September 7-9, 2009. Proceedings. Lecture Notes in Computer Science, vol. 5780, Springer, pp. 67-78. https://doi.org/10.1007/978-3-642-04031-3_7

Using higher-order dynamic Bayesian networks to model periodic data from the circadian clock of Arabidopsis Thaliana. / Daly, Ronan; Edwards, Kieron D.; O'Neill, John S et al.
Pattern Recognition in Bioinformatics: 4th IAPR International Conference, PRIB 2009, Sheffield, UK, September 7-9, 2009. Proceedings. Springer, 2009. p. 67-78 (Lecture Notes in Computer Science; Vol. 5780).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Girolami, Mark

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AB - 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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Daly R, Edwards KD, O'Neill JS, Aitken S , Millar A, Girolami M. Using higher-order dynamic Bayesian networks to model periodic data from the circadian clock of Arabidopsis Thaliana. In Pattern Recognition in Bioinformatics: 4th IAPR International Conference, PRIB 2009, Sheffield, UK, September 7-9, 2009. Proceedings. Springer. 2009. p. 67-78. (Lecture Notes in Computer Science). doi: 10.1007/978-3-642-04031-3_7

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

Abstract

Publication series

Access to Document

Other files and links

Fingerprint

Ontology views for collaborative ontology development & resources integration

SynthSys; formerly CSBE: Centre for Systems Biology at Edinburgh

Modelling circadian gene regulation in Arabidopsis thaliana

Circadian expression of genes: modelling the Arabidopsis circadian clock

Cite this

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

Abstract

Publication series

Access to Document

Other files and links

Fingerprint

Projects

Ontology views for collaborative ontology development & resources integration

SynthSys; formerly CSBE: Centre for Systems Biology at Edinburgh

Modelling circadian gene regulation in Arabidopsis thaliana

Datasets

Circadian expression of genes: modelling the Arabidopsis circadian clock

Cite this