Skip Navigation

This Article
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow FREE Full Text (Screen PDF)
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 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 (10)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Wang, J.
Right arrow Articles by Hovig, E.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Wang, J.
Right arrow Articles by Hovig, E.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Bioinformatics Vol. 19 no. 17 2003
pages 2210-2211
© 2003 Oxford University Press

MGraph: graphical models for microarray data analysis

Junbai Wang *, Ola Myklebost and Eivind Hovig

Tumor Biology Department, The Norwegian Radium Hospital, Montebello, 0310 Oslo, Norway

Received on January 16, 2003 ; revised on April 3, 2003 ; accepted on May 21, 2003

Summary: This paper introduces a MATLAB toolbox, MGraph, which applies graphical models as a natural environment to formulate and solve problems in microarray data analysis. MGraph with its graphical interface allows the user to predict genetic regulatory networks by a graphical gaussian model (GGM), and to quantify the effects of different experimental treatment conditions on gene expression profiles by a graphical log-linear model (GLM). The power of graphical models was explored and illustrated through two example applications. First, four MAPK pathways in yeast were meaningfully reconstructed through GGM. Second, GLM was used to quantify the contributions of sex, genotype and age to transcriptional variance in Drosophila melanogaster. This application may provide a valuable aid in the prediction of genetic regulatory networks, as well as in investigations of various experimental conditions that affect global gene expression profiles.

Availability: The MATLAB program MGraph is freely available at http://www.uio.no/~junbaiw/mgraph/mgraph.html for academics.

Contact: junbai.wang{at}radium.uio.no

* To whom correspondence should be addressed.


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
 Mol. Cell. ProteomicsHome page
M. Maziarz, C. Chung, D. J. Drucker, and A. Emili
Integrating Global Proteomic and Genomic Expression Profiles Generated from Islet {alpha} Cells: Opportunities and Challenges to Deriving Reliable Biological Inferences
Mol. Cell. Proteomics, April 1, 2005; 4(4): 458 - 474.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
J. Schafer and K. Strimmer
An empirical Bayes approach to inferring large-scale gene association networks
Bioinformatics, March 15, 2005; 21(6): 754 - 764.
[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.