ANN-Spec: a method for discovering transcription factor binding sites with improved specificity.
ANN-Spec: a method for discovering transcription factor binding sites with improved specificity.
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DOI：
10.1142/9789814447331_0044
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发表时间：
1999-12
影响因子：
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通讯作者：
Christopher T. Workman;G. Stormo
中科院分区：
文献类型：
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作者：
Christopher T. Workman;G. Stormo
本文介绍了一种机器学习算法ANN-Spec及其在发现DNA序列中未缺口模式中的应用。该方法利用人工神经网络和吉布斯抽样方法来定义DNA结合蛋白的特异性。ANN-Spec搜索一个简单网络（或权重矩阵）的参数，与背景序列集相比，该网络将最大化阳性集结合序列的特异性。用所得权重矩阵找到阳性数据集中的结合位点，然后将这些位点用于定义局部多序列比对。训练复杂度为O（lN），其中l是模式的宽度，N是正训练数据的大小。文中还对ANN-Spec和几个相关程序进行了定量比较。比较表明，ANN-Spec在使用背景数据集进行训练时发现了更高特异性的模式。该程序和文档可从UNIX系统的作者那里获得。
This work describes ANN-Spec, a machine learning algorithm and its application to discovering un-gapped patterns in DNA sequence. The approach makes use of an Artificial Neural Network and a Gibbs sampling method to define the Specificity of a DNA-binding protein. ANN-Spec searches for the parameters of a simple network (or weight matrix) that will maximize the specificity for binding sequences of a positive set compared to a background sequence set. Binding sites in the positive data set are found with the resulting weight matrix and these sites are then used to define a local multiple sequence alignment. Training complexity is O(lN) where l is the width of the pattern and N is the size of the positive training data. A quantitative comparison of ANN-Spec and a few related programs is presented. The comparison shows that ANN-Spec finds patterns of higher specificity when training with a background data set. The program and documentation are available from the authors for UNIX systems.