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A signaling pathway analysis model based on Kullback-Leibler divergence

机译:基于Kullback-Leibler散度的信号通路分析模型

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Abnormal regulation of signaling pathways is the key factor to cause disease. Many works focus on identifying the significantly differential pathways between diseases and normal samples via microarray gene expression datasets. However, it is general for exiting methods to concentrate on the difference of pathway components, either the expression or correlation among genes in a given pathway. Thus this will ignore the overall change of pathway. Here we present a powerful analysis model based on the concept of Kullback-Leibler divergence, which mainly measure the difference between two probability distributions of regulation capacity well. We compared our approach with other three classical algorithms on four different human expression datasets, and the results indicate that the capability of our method in detecting disturbed pathways is superior to previous approaches. In conclusion, via introducing the idea of Kullback-Leibler divergence, measure the whole difference of pathway from an overall perspective will provide a complementary analysis framework of pathway analysis.
机译:信号通路的异常调节是引起疾病的关键因素。许多工作致力于通过微阵列基因表达数据集识别疾病与正常样品之间的显着差异途径。然而,通常存在的方法集中于途径组分的差异,即给定途径中基因之间的表达或相关性。因此,这将忽略路径的整体变化。在这里,我们提出了一个基于Kullback-Leibler散度概念的强大分析模型,该模型主要很好地测量了两种调节能力概率分布之间的差异。我们在四个不同的人类表达数据集上将我们的方法与其他三个经典算法进行了比较,结果表明我们的方法在检测受干扰途径方面的能力优于以前的方法。总之,通过引入Kullback-Leibler散度的思想,从整体角度衡量路径的整体差异将提供路径分析的补充分析框架。

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