Skip Navigation



Bioinformatics Advance Access published online on July 15, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl364
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow Supplementary data
Right arrow All Versions of this Article:
22/17/2129    most recent
btl364v1
Right arrow Alert me when this article is cited
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 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 arrowRequest Permissions
Google Scholar
Right arrow Articles by Zhao, W.
Right arrow Articles by Dougherty, E. R.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Zhao, W.
Right arrow Articles by Dougherty, E. R.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received January 12, 2006
Revised May 27, 2006
Accepted June 29, 2006

Article

Inferring gene regulatory networks from time series data using the minimum description length principle

Wentao Zhao 1 *, Erchin Serpedin 1, and Edward R. Dougherty 2

1 Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas 77843-3128, USA
2 Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas 77843-3128, USA; Translational Genomics Research Institute, 400 North Fifth Street, Suite 1600, Phoenix, Arizona 85004, USA

* To whom correspondence should be addressed.
Wentao Zhao, E-mail: wtzhao{at}ece.tamu.edu


  Abstract

Motivation: A central question in reverse engineering of genetic networks consists in determining the dependencies and regulating relationships among genes. This paper addresses the problem of inferring genetic regulatory networks from time series gene-expression profiles. By adopting a probabilistic modeling framework compatible with the family of models represented by dynamic Bayesian networks and probabilistic Boolean networks, this paper proposes a network inference algorithm to recover not only the direct gene connectivity but also the regulating orientations.

Results: Based on the Minimum Description Length (MDL) principle, a novel network inference algorithm is proposed that greatly shrinks the search space for graphical solutions and achieves a good trade-off between modeling complexity and data fitting. Simulation results show that the algorithm achieves good performance in the case of synthetic networks. Compared with existing state-of-the-art results in the literature, the proposed algorithm exceptionally excels in efficiency, accuracy, robustness and scalability. Given a time series data set for drosophila melanogaster, the paper proposes a genetic regulatory network involved in drosophila's muscle development.

Availability: is available from the authors.

Associate Editor: Jonathan Wren
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. Kim, D. G. Bates, I. Postlethwaite, P. Heslop-Harrison, and K.-H. Cho
Linear time-varying models can reveal non-linear interactions of biomolecular regulatory networks using multiple time-series data
Bioinformatics, May 15, 2008; 24(10): 1286 - 1292.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
A. Li and S. Horvath
Network neighborhood analysis with the multi-node topological overlap measure
Bioinformatics, January 15, 2007; 23(2): 222 - 231.
[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.