A boosting approach for motif modeling using ChIP-chip data

Authors:
Pengyu Hong;X. Shirley Liu;Qing Zhou;Xin Lu;Jun S. Liu;Wing H. Wong
Affiliations:
Department of Statistics, Harvard University Cambridge, MA 02138, USA;Department of Biostatistics, Harvard School of Public Health Boston, MA 02115, USA;Department of Statistics, Harvard University Cambridge, MA 02138, USA;Department of Biostatistics, Harvard School of Public Health Boston, MA 02115, USA;Department of Statistics, Harvard University Cambridge, MA 02138, USA;Department of Statistics, Harvard University Cambridge, MA 02138, USA
Venue:
Bioinformatics
Year:
2005

Citing 0
Cited 5

Multi-resolution Boosting for Classification and Regression Problems

PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Nucleosome occupancy information improves de novo motif discovery

RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
Gene selection and prediction for cancer classification using support vector machines with a reject option

Computational Statistics & Data Analysis
Computational molecular biology of genome expression and regulation

PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Functional characterization of drug-protein interactions network

Intelligent Data Analysis

Quantified Score

Hi-index	3.84

Visualization

Abstract

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