Skip Navigation


Bioinformatics Advance Access originally published online on September 27, 2006
Bioinformatics 2006 22(23):2890-2897; doi:10.1093/bioinformatics/btl492
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow All Versions of this Article:
22/23/2890    most recent
btl492v1
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 (5)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Huttenhower, C.
Right arrow Articles by Troyanskaya, O. G.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Huttenhower, C.
Right arrow Articles by Troyanskaya, O. G.
Social Bookmarking
 Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

A scalable method for integration and functional analysis of multiple microarray datasets

Curtis Huttenhower , Matt Hibbs , Chad Myers and Olga G. Troyanskaya *

Department of Computer Science, Lewis-Sigler Institute for Integrative Genomics, Princeton University Princeton, NJ, USA

*To whom correspondence should be addressed.

Motivation: The diverse microarray datasets that have become available over the past several years represent a rich opportunity and challenge for biological data mining. Many supervised and unsupervised methods have been developed for the analysis of individual microarray datasets. However, integrated analysis of multiple datasets can provide a broader insight into genetic regulation of specific biological pathways under a variety of conditions.

Results: To aid in the analysis of such large compendia of microarray experiments, we present Microarray Experiment Functional Integration Technology (MEFIT), a scalable Bayesian framework for predicting functional relationships from integrated microarray datasets. Furthermore, MEFIT predicts these functional relationships within the context of specific biological processes. All results are provided in the context of one or more specific biological functions, which can be provided by a biologist or drawn automatically from catalogs such as the Gene Ontology (GO). Using MEFIT, we integrated 40 Saccharomyces cerevisiae microarray datasets spanning 712 unique conditions. In tests based on 110 biological functions drawn from the GO biological process ontology, MEFIT provided a 5% or greater performance increase for 54 functions, with a 5% or more decrease in performance in only two functions.

Contact: ogt{at}cs.princeton.edu

Supplementary information: Supplementary data, a collection of predictions made by MEFIT and software implementing MEFIT are available online at http://function.princeton.edu/mefit/.


Received on July 31, 2006; revised on September 5, 2006; accepted on September 19, 2006

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
C. Huttenhower, M. Schroeder, M. D Chikina, and O. G. Troyanskaya
The Sleipnir library for computational functional genomics
Bioinformatics, July 1, 2008; 24(13): 1559 - 1561.
[Abstract] [Full Text] [PDF]


Home page
BioinformaticsHome page
C. Huttenhower and O.G. Troyanskaya
Assessing the functional structure of genomic data
Bioinformatics, July 1, 2008; 24(13): i330 - i338.
[Abstract] [Full Text] [PDF]


Home page
Nucleic Acids ResHome page
D. Aguilar, L. Skrabanek, S. S. Gross, B. Oliva, and F. Campagne
Beyond tissueInfo: functional prediction using tissue expression profile similarity searches
Nucleic Acids Res., June 1, 2008; 36(11): 3728 - 3737.
[Abstract] [Full Text] [PDF]


Home page
BioinformaticsHome page
P. Sykacek, R. Clarkson, C. Print, R. Furlong, and G. Micklem
Bayesian modelling of shared gene function
Bioinformatics, August 1, 2007; 23(15): 1936 - 1944.
[Abstract] [Full Text] [PDF]


Home page
BioinformaticsHome page
Y. Shi, M. Klustein, I. Simon, T. Mitchell, and Z. Bar-Joseph
Continuous hidden process model for time series expression experiments
Bioinformatics, July 1, 2007; 23(13): i459 - i467.
[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.