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首页> 外文会议>International Workshop on Pattern Recognition in Bioinformatics >Using Higher-Order Dynamic Bayesian Networks to Model Periodic Data from the Circadian Clock of Arabidopsis Thaliana
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Using Higher-Order Dynamic Bayesian Networks to Model Periodic Data from the Circadian Clock of Arabidopsis Thaliana

机译：使用高阶动态贝叶斯网络从拟南芥昼夜昼夜昼夜模拟定期数据

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摘要

Modelling gene regulatory networks in organisms is an important task that has recently become possible due to large scale assays using technologies such as microarrays. In this paper, the circadian clock of Arabidopsis thaliana is modelled by fitting dynamic Bayesian networks to luminescence data gathered from experiments. This work differs from previous modelling attempts by using higher-order dynamic Bayesian networks to explicitly model the time lag between the various genes being expressed. In order to achieve this goal, new techniques in preprocessing the data and in evaluating a learned model are proposed. It is shown that it is possible, to some extent, to model these time delays using a higher-order dynamic Bayesian network.
机译：在生物体中建模基因监管网络是最近可能由于使用微阵列等技术的大规模测定而成为可能的重要任务。本文通过拟合动态贝叶斯网络来建模拟南芥拟南芥昼夜模型，以对从实验收集的发光数据进行建模。通过使用高阶动态贝叶斯网络来明确地模拟所表达的各种基因之间的时间滞后，这种工作与先前的建模尝试不同。为了实现这一目标，提出了预处理数据和评估学习模型的新技术。结果表明，在某种程度上可以使用高阶动态贝叶斯网络来模拟这些时间延迟。

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