基因调控网络的逆向工程:比较研究。

Hendrik Hache, Hans Lehrach, Ralf Herwig
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引用次数: 85

摘要

基因调控网络的逆向工程一直是生物信息学研究的热点,因为它是从探索性基因表达分析到致病性基因表达分析的中间步骤。近年来提出了许多方法,导致了广泛的数学方法。在实践中,不同的数学方法会产生不同的网络结构,因此,用户评估这些算法的性能是非常重要的。我们对六种不同的逆向工程方法进行了比较研究,包括相关网络、神经网络和贝叶斯网络。我们的方法包括生成定义的基准数据,用不同的方法分析这些数据,以及通过统计分析评估算法的性能。性能是根据网络大小和噪音水平来判断的。对比研究结果表明,神经网络方法是目前研究中表现最好的方法。
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Reverse engineering of gene regulatory networks: a comparative study.

Reverse engineering of gene regulatory networks has been an intensively studied topic in bioinformatics since it constitutes an intermediate step from explorative to causative gene expression analysis. Many methods have been proposed through recent years leading to a wide range of mathematical approaches. In practice, different mathematical approaches will generate different resulting network structures, thus, it is very important for users to assess the performance of these algorithms. We have conducted a comparative study with six different reverse engineering methods, including relevance networks, neural networks, and Bayesian networks. Our approach consists of the generation of defined benchmark data, the analysis of these data with the different methods, and the assessment of algorithmic performances by statistical analyses. Performance was judged by network size and noise levels. The results of the comparative study highlight the neural network approach as best performing method among those under study.

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