Skip Navigation

Bioinformatics 2005 21(Suppl 2):ii93-ii100; doi:10.1093/bioinformatics/bti1116
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 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 (4)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Yoon, S.
Right arrow Articles by De Micheli, G.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Yoon, S.
Right arrow Articles by De Micheli, G.
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}oxfordjournals.org

Prediction of regulatory modules comprising microRNAs and target genes

Sungroh Yoon 1,* and Giovanni De Micheli 2

1Computer Systems Laboratory, Stanford University Stanford, CA 94305, USA
2Integrated System Center EPF Lausanne, Switzerland

*To whom correspondence should be addressed.

Motivation: MicroRNAs (miRNAs) are small endogenous RNAs that can play important regulatory roles via the RNA-interference pathway by targeting mRNAs for cleavage or translational repression. We propose a computational method to predict miRNA regulatory modules (MRMs) or groups of miRNAs and target genes that are believed to participate cooperatively in post-transcriptional gene regulation.

Results: We tested our method with the human genes and miRNAs, predicting 431 MRMs. We analyze a module with genes: BTG2, WT1, PPM1D, PAK7 and RAB9B, and miRNAs: miR-15a and miR-16. Review of the literature and annotation with Gene Ontology terms reveal that the roles of these genes can indeed be closely related in specific biological processes, such as gene regulation involved in breast, renal and prostate cancers. Furthermore, it has been reported that miR-15a and miR-16 are deleted together in certain types of cancer, suggesting a possible connection between these miRNAs and cancers. Given that most known functionalities of miRNAs are related to negative gene regulation, extending our approach and exploiting the insight thus obtained may provide clues to achieving practical accuracy in the reverse-engineering of gene regulatory networks.

Availability: A list of predicted modules is available from the authors upon request.

Contact: sryoon{at}stanford.edu


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.-G. Joung, K.-B. Hwang, J.-W. Nam, S.-J. Kim, and B.-T. Zhang
Discovery of microRNA mRNA modules via population-based probabilistic learning
Bioinformatics, May 1, 2007; 23(9): 1141 - 1147.
[Abstract] [Full Text] [PDF]

Home page
 Cancer Res.Home page
H. Kawakubo, E. Brachtel, T. Hayashida, G. Yeo, J. Kish, A. Muzikansky, P. D. Walden, and S. Maheswaran
Loss of B-cell translocation gene-2 in estrogen receptor-positive breast carcinoma is associated with tumor grade and overexpression of cyclin d1 protein.
Cancer Res., July 15, 2006; 66(14): 7075 - 7082.
[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.