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Bioinformatics Advance Access published online on March 22, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti388
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received May 18, 2004
Revised March 8, 2005
Accepted March 9, 2005

Article

Inferring genetic regulatory logic from expression data

Svetlana Bulashevska 1* and Roland Eils 2

1 Division "Theoretical Bioinformatics", German Cancer Research Center, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
2 Division "Theoretical Bioinformatics", German Cancer Research Center, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; Department "Bioinformatics and Functional Genomics", Institute of Pharmacy and Molecular Biotechnology (IPMB), University of Heidelberg

* To whom correspondence should be addressed.
Svetlana Bulashevska, E-mail: s.bulashevska{at}dkfz.de


  Abstract

Motivation: High throughput molecular genetics methods allow to collect data about expression of genes at different time points and under different conditions. The challenge is to infer gene regulatory interactions from this data and to get insight into the mechanisms of genetic regulation.

Results: We propose a model for genetic regulatory interactions, which has a biologically motivated Boolean logic semantics, but is of probabilistic nature, hence being able to confront noisy biological process and data. We propose a method for learning the model from data based on Bayesian approach and utilizing Gibbs sampling. We tested our method with previously published data of the S.cerevisiae cell cycle and found relations between genes consistent with biological knowledge.

Availability: The code for the software BUGS is available upon request.

Supplementary information: http://oslo.inet.dkfz-heidelberg.de/ibios_old/people/bulashev/Supplement/.


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