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Bioinformatics Advance Access originally published online on October 28, 2004
Bioinformatics 2005 21(7):1172-1179; doi:10.1093/bioinformatics/bti096
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© The Author 2004. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

Finding regulatory modules through large-scale gene-expression data analysis

M. Kloster 1,2, C. Tang 2,3,* and N.S. Wingreen 2,4

1Department of Physics, Princeton University Princeton, NJ 08544, USA
2NEC Laboratories America, Inc. Princeton, NJ 08540, USA
3Center for Theoretical Biology, Peking University Beijing 100871, China
4Department of Molecular Biology, Princeton University Princeton, NJ 08544, USA

*To whom correspondence should be addressed.

Motivation: The use of gene microchips has enabled a rapid accumulation of gene-expression data. One of the major challenges of analyzing this data is the diversity, in both size and signal strength, of the various modules in the gene regulatory networks of organisms.

Results: Based on the iterative signature algorithm [Bergmann,S., Ihmels,J. and Barkai,N. (2002) Phys. Rev. E 67, 031902], we present an algorithm—the progressive iterative signature algorithm (PISA)—that, by sequentially eliminating modules, allows unsupervised identification of both large and small regulatory modules. We applied PISA to a large set of yeast gene-expression data, and, using the Gene Ontology database as a reference, found that the algorithm is much better able to identify regulatory modules than methods based on high-throughput transcription-factor binding experiments or on comparative genomics.

Contact: tang{at}nec-labs.com


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