Skip Navigation



Bioinformatics Advance Access published online on May 8, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn220
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow Supplementary Data
Right arrow All Versions of this Article:
24/13/1510    most recent
btn220v1
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 Zampieri, M.
Right arrow Articles by Altafini, C.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Zampieri, M.
Right arrow Articles by Altafini, C.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Discerning static and causal interactions in genome-wide reverse engineering problems

M. Zampieri , N. Soranzo and C. Altafini *

aSISSA-ISAS, International School for Advanced Studies, via Beirut 2-4, 34014 Trieste, Italy

*To whom correspondence should be addressed. C. Altafini, E-mail: altafini{at}sissa.it


  Abstract

Motivation: In the past years devising methods for discovering gene regulatory mechanisms at a genome-wide level has become a fundamental topic in the field of systems biology. The aim is to infer gene-gene interactions in an increasingly sophisticated and reliable way through the continuous improvement of reverse engineering algorithms exploiting microarray data.

Results: This work is inspired by the several studies suggesting that co-expression is mostly related to "static" stable binding relationships, like belonging to the same protein complex, rather than other types of interactions more of a "causal" and transient nature (e.g. transcription factor–binding site interactions). The aim of this work is to verify if direct or conditional network inference algorithms (e.g. Pearson correlation for the former, partial Pearson correlation for the latter) are indeed useful in discerning static from causal dependencies in artificial and real gene networks (derived from E.coli and S.cerevisiae).

Contact: altafini{at}sissa.it

Associate Editor: Dr. Olga Troyanskaya

Received on January 16, 2008; revised on April 17, 2008; accepted on May 1, 2008

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
 Nucleic Acids ResHome page
A. Ruepp, B. Waegele, M. Lechner, B. Brauner, I. Dunger-Kaltenbach, G. Fobo, G. Frishman, C. Montrone, and H.-W. Mewes
CORUM: the comprehensive resource of mammalian protein complexes--2009
Nucleic Acids Res., November 1, 2009; (2009) gkp914v1.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
A. Reverter and E. K. F. Chan
Combining partial correlation and an information theory approach to the reversed engineering of gene co-expression networks
Bioinformatics, November 1, 2008; 24(21): 2491 - 2497.
[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.