Skip Navigation

Bioinformatics 2005 21(Suppl 1):i213-i221; doi:10.1093/bioinformatics/bti1049
This Article
Right arrow FREE Full Text (Print PDF) Freely available
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 Hu, H.
Right arrow Articles by Zhou, X. J.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Hu, H.
Right arrow Articles by Zhou, X. J.
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

Mining coherent dense subgraphs across massive biological networks for functional discovery

Haiyan Hu 1, Xifeng Yan 2, Yu Huang 1, Jiawei Han 2 and Xianghong Jasmine Zhou 1,*

1Program in Molecular and Computational Biology, University of Southern California Los Angeles, CA 90089, USA
2Department of Computer Science, University of Illinois at Urbana-Champaign Urbana, IL 61801, USA

*To whom correspondence should be addressed.

Motivation: The rapid accumulation of biological network data translates into an urgent need for computational methods for graph pattern mining. One important problem is to identify recurrent patterns across multiple networks to discover biological modules. However, existing algorithms for frequent pattern mining become very costly in time and space as the pattern sizes and network numbers increase. Currently, no efficient algorithm is available for mining recurrent patterns across large collections of genome-wide networks.

Results: We developed a novel algorithm, CODENSE, to efficiently mine frequent coherent dense subgraphs across large numbers of massive graphs. Compared with previous methods, our approach is scalable in the number and size of the input graphs and adjustable in terms of exact or approximate pattern mining. Applying CODENSE to 39 co-expression networks derived from microarray datasets, we discovered a large number of functionally homogeneous clusters and made functional predictions for 169 uncharacterized yeast genes.

Availability: http://zhoulab.usc.edu/CODENSE/

Contact: xjzhou{at}usc.edu

Received on January 15, 2005; accepted on March 27, 2005

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
X. Yan, M. R. Mehan, Y. Huang, M. S. Waterman, P. S. Yu, and X. J. Zhou
A graph-based approach to systematically reconstruct human transcriptional regulatory modules
Bioinformatics, July 1, 2007; 23(13): i577 - i586.
[Abstract] [Full Text] [PDF]

Home page
 Nucleic Acids ResHome page
F. Pan, C.-H. Chiu, S. Pulapura, M. R. Mehan, J. Nunez-Iglesias, K. Zhang, K. Kamath, M. S. Waterman, C. E. Finch, and X. J. Zhou
Gene Aging Nexus: a web database and data mining platform for microarray data on aging
Nucleic Acids Res., January 12, 2007; 35(suppl_1): D756 - D759.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
X. Chen, M. Chen, and K. Ning
BNArray: an R package for constructing gene regulatory networks from microarray data by using Bayesian network
Bioinformatics, December 1, 2006; 22(23): 2952 - 2954.
[Abstract] [Full Text] [PDF]

Home page
 Brief BioinformHome page
T. Aittokallio and B. Schwikowski
Graph-based methods for analysing networks in cell biology
Brief Bioinform, September 1, 2006; 7(3): 243 - 255.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
F. Pan, K. Kamath, K. Zhang, S. Pulapura, A. Achar, J. Nunez-Iglesias, Y. Huang, X. Yan, J. Han, H. Hu, et al.
Integrative Array Analyzer: a software package for analysis of cross-platform and cross-species microarray data
Bioinformatics, July 1, 2006; 22(13): 1665 - 1667.
[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.