期刊
BIOINFORMATICS
卷 21, 期 11, 页码 2636-2643出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/bti402
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- NCI NIH HHS [P20-CA96470] Funding Source: Medline
- NHGRI NIH HHS [HG02341] Funding Source: Medline
- NIGMS NIH HHS [R01 GM067250, GM67250] Funding Source: Medline
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.
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