Skip Navigation



Bioinformatics Advance Access published online on May 5, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm129
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow All Versions of this Article:
23/12/1537    most recent
btm129v2
btm129v1
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 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 Wei, Z.
Right arrow Articles by Li, H.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Wei, Z.
Right arrow Articles by Li, H.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

A Markov Random Field Model for Network-based Analysis of Genomic Data

Zhi Wei a and Hongzhe Li a,b

aGenomics and Computational Biology Graduate Group and bDepartment of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104

*To whom correspondence should be addressed. Prof. Hongzhe Li, E-mail: hongzhe{at}mail.med.upenn.edu


  Abstract

Motivation: A central problem in genomic research is the identification of genes and pathways involved in diseases and other biological processes. The genes identified or the univariate test statistics are often linked to known biological pathways through gene set enrichment analysis in order to identify the pathways involved. However, most of the procedures for identifying differentially expressed genes do not utilize the known pathway information in the phase of identifying such genes. In this paper, we develop a Markov random field (MRF)-based method for identifying genes and subnetworks that are related to diseases. Such a procedure models the dependency of the differential expression patterns of genes on the networks using a local discrete MRF model.

Results: Simulation studies indicated that the method is quite effective in identifying genes and subnetworks that are related to disease and has higher sensitivity and lower false discovery rates than the commonly used procedures that do not use the pathway structure information. Applications to two breast cancer microarray gene expression datasets identified several subnetworks on several of the KEGG transcriptional pathways that are related to breast cancer recurrence or survival due to breast cancer.

Conclusions: The proposed MRF-based model efficiently utilizes the known pathway structures in identifying the differentially expressed genes and the subnetworks that might be related to phenotype. As more biological networks are identified and documented in databases, the proposed method should find more applications in identifying the subnetworks that are related to diseases and other biological processes.

Associate Editor: Dr. Olga Troyanskaya

Received on February 1, 2007; revised on March 26, 2007; accepted on March 27, 2007

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
Z. Wei, W. Sun, K. Wang, and H. Hakonarson
Multiple testing in genome-wide association studies via hidden Markov models
Bioinformatics, November 1, 2009; 25(21): 2802 - 2808.
[Abstract] [Full Text] [PDF]

Home page
 BiostatisticsHome page
H. Li, Z. Wei, and J. Maris
A hidden Markov random field model for genome-wide association studies
Biostat., October 12, 2009; (2009) kxp043v1.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
W. Pan
Network-based multiple locus linkage analysis of expression traits
Bioinformatics, June 1, 2009; 25(11): 1390 - 1396.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
B. Zhang, H. Li, R. B. Riggins, M. Zhan, J. Xuan, Z. Zhang, E. P. Hoffman, R. Clarke, and Y. Wang
Differential dependency network analysis to identify condition-specific topological changes in biological networks
Bioinformatics, February 15, 2009; 25(4): 526 - 532.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
J. Noirel, G. Sanguinetti, and P. C. Wright
Identifying differentially expressed subnetworks with MMG
Bioinformatics, December 1, 2008; 24(23): 2792 - 2793.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
C. Li and H. Li
Network-constrained regularization and variable selection for analysis of genomic data
Bioinformatics, May 1, 2008; 24(9): 1175 - 1182.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
G. Sanguinetti, J. Noirel, and P. C. Wright
MMG: a probabilistic tool to identify submodules of metabolic pathways
Bioinformatics, April 15, 2008; 24(8): 1078 - 1084.
[Abstract] [Full Text] [PDF]

Home page
 Brief Funct Genomic ProteomicHome page
J. Noirel, S. Y. Ow, G. Sanguinetti, A. Jaramillo, and P. C. Wright
Automated extraction of meaningful pathways from quantitative proteomics data
Brief Funct Genomic Proteomic, March 7, 2008; (2008) eln011v1.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
P. Wei and W. Pan
Incorporating gene networks into statistical tests for genomic data via a spatially correlated mixture model
Bioinformatics, February 1, 2008; 24(3): 404 - 411.
[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.