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首页> 外文会议>Evolutionary computation, machine learning and data mining in bioinformatics >Applying Linear Models to Learn Regulation Programs in a Transcription Regulatory Module Network
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Applying Linear Models to Learn Regulation Programs in a Transcription Regulatory Module Network

机译：将线性模型应用于转录调控模块网络中的调控程序

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摘要

The module network method has been widely used to infer transcrip-tional regulatory network from gene expression data. A common strategy of module network learning algorithms is to apply regression trees to infer the regulation program of a module. In this work we propose to apply linear models to fulfill this task. The novelty of our method is to extract the contrast in which a module's genes are most significantly differentially expressed. Consequently, the process of learning the regulation program for the module becomes one of identifying transcription factors that are also differentially expressed in this contrast. The effectiveness of our algorithm is demonstrated by the experiments in a yeast benchmark dataset.
机译：模块网络方法已被广泛用于从基因表达数据推断转录调控网络。模块网络学习算法的常见策略是应用回归树来推断模块的调节程序。在这项工作中，我们建议应用线性模型来完成此任务。我们方法的新颖性在于提取对比度，在该对比度中模块的基因被最显着地差异表达。因此，学习模块的调节程序的过程成为识别也以这种对比差异表达的转录因子之一。酵母基准数据集中的实验证明了我们算法的有效性。

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