A boosting approach for motif modeling using ChIP-chip data.
BACKGROUND
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.
Hong
Pengyu
P
Department of Statistics, Harvard University, Cambridge, MA 02138, USA.
Liu
X Shirley
XS
Zhou
Qing
Q
Lu
Xin
X
Liu
Jun S
JS
Wong
Wing H
WH
D000465:Algorithms; D020816:Amino Acid Motifs; D001665:Binding Sites; D004247:DNA / Q000032:analysis / Q000737:chemistry*; D030561:Databases, Nucleic Acid; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011485:Protein Binding; D029701:Saccharomyces cerevisiae Proteins / Q000032:analysis / Q000737:chemistry* / Q000145:classification; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*; D012689:Sequence Homology, Nucleic Acid; D014157:Transcription Factors / Q000032:analysis / Q000737:chemistry* / Q000145:classification
D029701:Saccharomyces cerevisiae Proteins; D014157:Transcription Factors; D004247:DNA