A boosting approach for motif modeling using ChIP-chip data
A boosting approach for motif modeling using ChIP-chip data
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DOI：
10.1093/bioinformatics/bti402
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发表时间：
2005-06-01
期刊：
影响因子：
5.8
通讯作者：
Wong, WH
中科院分区：
文献类型：
--
作者：
Hong, PY;Liu, XS;Wong, WH
动机：为转录因子（TF）构建一个精确的结合模型对于区分其真正的结合靶点和虚假靶点至关重要。这是理解基因调控的重要一步。
结果：本文描述了一种对转录因子 - DNA结合进行建模的提升方法。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转录因子 - 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转录因子结合分类器，从而产生一个非线性的结合决策规则。所提出的方法应用于酿酒酵母的染色质免疫沉淀 - 芯片（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上有显著提高。
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.