Skip Navigation


Bioinformatics Advance Access originally published online on April 26, 2007
Bioinformatics 2007 23(13):1599-1606; doi:10.1093/bioinformatics/btm149
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow All Versions of this Article:
23/13/1599    most recent
btm149v1
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 arrowRequest Permissions
Google Scholar
Right arrow Articles by Fishel, I.
Right arrow Articles by Ruppin, E.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Fishel, I.
Right arrow Articles by Ruppin, E.
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

Meta-analysis of gene expression data: a predictor-based approach

Irit Fishel 1, Alon Kaufman 2 and Eytan Ruppin 1,3,*

1School of Medicine, Tel-Aviv University, Tel-Aviv 69978, 2Interdisciplinary Center for Neural Computation, Hebrew University, Jerusalem 91904 and 3School of Computer Science, Tel-Aviv University, Tel-Aviv 69978, Israel

*To whom correspondence should be addressed.


  Abstract

Motivation: With the increasing availability of cancer microarray data sets there is a growing need for integrative computational methods that evaluate multiple independent microarray data sets investigating a common theme or disorder. Meta-analysis techniques are designed to overcome the low sample size typical to microarray experiments and yield more valid and informative results than each experiment separately.

Results: We propose a new meta-analysis technique that aims at finding a set of classifying genes, whose expression level may be used to answering the classification question in hand. Specifically, we apply our method to two independent lung cancer microarray data sets and identify a joint core subset of genes which putatively play an important role in tumor genesis of the lung. The robustness of the identified joint core set is demonstrated on a third unseen lung cancer data set, where it leads to successful classification using very few top-ranked genes. Identifying such a set of genes is of significant importance when searching for biologically meaningful biomarkers.

Contact: ruppin{at}post.tau.ac.il

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Trey Ideker

Received on November 2, 2006; revised on March 18, 2007; accepted on April 14, 2007

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




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.