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Bioinformatics Advance Access originally published online on April 7, 2005
Bioinformatics 2005 21(11):2636-2643; doi:10.1093/bioinformatics/bti402
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

A boosting approach for motif modeling using ChIP-chip data

Pengyu Hong 1, X. Shirley Liu 2, Qing Zhou 1, Xin Lu 2, Jun S. Liu 1,2 and Wing H. Wong 1,2,*

1Department of Statistics, Harvard University Cambridge, MA 02138, USA
2Department of Biostatistics, Harvard School of Public Health Boston, MA 02115, USA

*To whom correspondence should be addressed.

Motivation: Building an accurate binding model for a transcription factor (TF) is essential to differentiate its true binding targets from those spurious ones. This is an important step toward understanding gene regulation.

Results: This paper describes a boosting approach to modeling TF–DNA binding. Different from the widely used weight matrix model, which predicts TF–DNA binding based on a linear combination of position-specific contributions, our approach builds a TF binding classifier by combining a set of weight matrix based classifiers, thus yielding a non-linear binding decision rule. The proposed approach was applied to the ChIP-chip data of Saccharomyces cerevisiae. When compared with the weight matrix method, our new approach showed significant improvements on the specificity in a majority of cases.

Contact: wwong{at}hsph.harvard.edu

Supplementary information: The software and the Supplementary data are available at http://biogibbs.stanford.edu/~hong2004/MotifBooster/.


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