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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >MINING TIME-DEPENDENT GENE FEATURES
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MINING TIME-DEPENDENT GENE FEATURES

机译：采矿时间依赖性基因特征

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This paper presents an application of the Independent Component Analysis (ICA) method to genomic data. In particular, experimentally produced perturbation effects over the E.coli bacterium are monitored through the changes of gene expression values observed at regular times, and until steady state has been reached. The aim is to control the response of the SOS system to DNA damage. We might assume that only part of the genetic regulatory network is affected directly by the perturbation conditions, as indirect cascade effects might also be present, and some genes may change just because of randomness. ICA decomposes the gene matrix and identifies groups of genes belonging to a certain estimated component by virtue of co-expression; it is of course of interest to establish co-regulation dynamics, which might underlie the captured correlation. Stronger forms of dependence, like Mutual Information, are thus computed and compared with linear correlation in order to validate the results and establish the role of the identified components in determining the network dynamics.
机译：本文介绍了独立分量分析（ICA）方法对基因组数据的应用。特别地，通过在常规时间观察到的基因表达值的变化来监测对大肠杆菌细菌的实验产生的扰动效应，直到达到稳态。目的是控制SOS系统对DNA损伤的响应。我们可能认为只有遗传调节网络的一部分受到扰动条件的影响，因为也存在间接级联效应，一些基因可能因随机性而改变。 ICA分解基因基质，并通过共表达鉴定属于某种估计成分的基因组;建立共同调节动态是有兴趣的，这可能会削弱捕获的相关性。因此，与线性相关性相比，与相互信息相比的更强的依赖性形式，以便验证结果并建立所识别的组件在确定网络动态时的作用。

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