Hong Pengyu, Liu X Shirley, Zhou Qing, Lu Xin, Liu Jun S, Wong Wing H
Department of Statistics, Harvard University, Cambridge, MA 02138, USA.
Bioinformatics. 2005 Jun 1;21(11):2636-43. doi: 10.1093/bioinformatics/bti402. Epub 2005 Apr 7.
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.
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.
构建准确的转录因子（TF）结合模型对于区分其真正的结合靶点与虚假靶点至关重要。这是理解基因调控的重要一步。
本文描述了一种用于TF-DNA结合建模的增强方法。与广泛使用的权重矩阵模型不同，权重矩阵模型基于位置特异性贡献的线性组合来预测TF-DNA结合，我们的方法通过组合一组基于权重矩阵的分类器来构建TF结合分类器，从而产生非线性结合决策规则。所提出的方法应用于酿酒酵母的ChIP-chip数据。与权重矩阵方法相比，我们的新方法在大多数情况下在特异性方面有显著提高。
