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Influence of Noise on the Inference of Dynamic Bayesian Networks from Short Time Series

Emmert-Streib, Frank ; Dehmer, Matthias ; Bakir, H. G. ; Mühlhäuser, Max
Ardil, Cemal (ed.) (2005):
Influence of Noise on the Inference of Dynamic Bayesian Networks from Short Time Series.
In: Proceedings Of World Academy Of Science, Engineering And Technology, 10, pp. 70-74, World Academy of Science, 8th Conference of the World Academy of Science, Engineering and Technology, Cracow, Poland, 16.-18.12.2005, ISSN 1307-6884, ISBN 975-98458-9-X,
[Conference or Workshop Item]

Abstract

In this paper we investigate the influence of external noise on the inference of network structures. The purpose of our simulations is to gain insights in the experimental design of microarray experiments to infer, e.g., transcription regulatory networks from microarray experiments. Here external noise means, that the dynamics of the system under investigation, e.g., temporal changes of mRNA concentration, is affected by measurement errors. Additionally to external noise another problem occurs in the context of microarray experiments. Practically, it is not possible to monitor the mRNA concentration over an arbitrary long time period as demanded by the statistical methods used to learn the underlying network structure. For this reason, we use only short time series to make our simulations more biologically plausible.

Item Type: Conference or Workshop Item
Erschienen: 2005
Editors: Ardil, Cemal
Creators: Emmert-Streib, Frank ; Dehmer, Matthias ; Bakir, H. G. ; Mühlhäuser, Max
Title: Influence of Noise on the Inference of Dynamic Bayesian Networks from Short Time Series
Language: English
Abstract:

In this paper we investigate the influence of external noise on the inference of network structures. The purpose of our simulations is to gain insights in the experimental design of microarray experiments to infer, e.g., transcription regulatory networks from microarray experiments. Here external noise means, that the dynamics of the system under investigation, e.g., temporal changes of mRNA concentration, is affected by measurement errors. Additionally to external noise another problem occurs in the context of microarray experiments. Practically, it is not possible to monitor the mRNA concentration over an arbitrary long time period as demanded by the statistical methods used to learn the underlying network structure. For this reason, we use only short time series to make our simulations more biologically plausible.

Series: Proceedings Of World Academy Of Science, Engineering And Technology
Series Volume: 10
Publisher: World Academy of Science
ISBN: 975-98458-9-X
Uncontrolled Keywords: Dynamic Bayesian networks, structure learning, gene networks, Markov chain Monte Carlo, microarray data
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Telecooperation
Event Title: 8th Conference of the World Academy of Science, Engineering and Technology
Event Location: Cracow, Poland
Event Dates: 16.-18.12.2005
Date Deposited: 31 Dec 2016 12:59
URL / URN: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.192...
Additional Information:

später ersch. in: International Journal of Bioengineering and Life Sciences Vol.1, No.10, 2007

Identification Number: EDBM:2005
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