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首页> 外文期刊>Journal of Computational Intelligence in Bioinformatics >Modelling Gene Regulatory Network from Microarray Data Using Modified Genetic Algorithm
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Modelling Gene Regulatory Network from Microarray Data Using Modified Genetic Algorithm

机译:使用改进的遗传算法从微阵列数据建模基因调控网络

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In this paper a modified Genetic Algorithm (GA) was used to model gene regulatory network from microarray data. GA can effectively model gene regulation and interaction to accurately reflect the underlying biology. However, this approach requires several generations and much computational power to identify the interacting genes. This paper uses some statistical techniques to reduce the search space, thereby reducing the number of generations. This will allow the algorithm to run in a shorter amount of time with minimal effect on the result. Our approach was tested on a set of genes whose regulatory functions are identified using fuzzy GRN algorithm. The method derives a regulatory network structure which is consistent with the network structure obtained using feed forward neural fuzzy network and fuzzy GRN algorithm. The inferred knowledge can be used to provide guidance for the experiments by suggesting likely relations inferred from the observed data.
机译:在本文中,使用改进的遗传算法(GA)从微阵列数据对基因调控网络进行建模。 GA可以有效地模拟基因调控和相互作用,以准确反映基础生物学。但是,这种方法需要几代人和大量的计算能力才能识别相互作用的基因。本文使用一些统计技术来减少搜索空间,从而减少世代数。这将使算法在更短的时间内运行,并且对结果的影响最小。我们的方法在一组基因的测试中进行了验证,这些基因的调控功能使用模糊GRN算法确定。该方法推导了与使用前馈神经模糊网络和模糊GRN算法获得的网络结构一致的监管网络结构。通过建议从观察到的数据推断出可能的关系,可以将推断出的知识用于实验指导。

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