Skip Navigation


Bioinformatics Advance Access originally published online on March 22, 2005
Bioinformatics 2005 21(11):2706-2713; doi:10.1093/bioinformatics/bti388
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow All Versions of this Article:
21/11/2706    most recent
bti388v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (15)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Bulashevska, S.
Right arrow Articles by Eils, R.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Bulashevska, S.
Right arrow Articles by Eils, R.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

Inferring genetic regulatory logic from expression data

Svetlana Bulashevska 1,* and Roland Eils 1,2

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

*To whom correspondence should be addressed.

Motivation: High-throughput molecular genetics methods allow the collection of data about the expression of genes at different time points and under different conditions. The challenge is to infer gene regulatory interactions from these data and to get an 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 a probabilistic nature, and is hence able to confront noisy biological processes and data. We propose a method for learning the model from data based on the Bayesian approach and utilizing Gibbs sampling. We tested our method with previously published data of the Saccharomyces cerevisiae cell cycle and found relations between genes consistent with biological knowledge.

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

Contact: s.bulashevska{at}dkfz.de

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


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 BioinformaticsHome page
J. Dingel and O. Milenkovic
List-decoding methods for inferring polynomials in finite dynamical gene network models
Bioinformatics, July 1, 2009; 25(13): 1686 - 1693.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
M. Elati, P. Neuvial, M. Bolotin-Fukuhara, E. Barillot, F. Radvanyi, and C. Rouveirol
LICORN: learning cooperative regulation networks from gene expression data
Bioinformatics, September 15, 2007; 23(18): 2407 - 2414.
[Abstract] [Full Text] [PDF]


Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.