Network-Based Predictions and Simulations by Biological State Space Models: Search for Drug Mode of Action
Network-Based Predictions and Simulations by Biological State Space Models: Search for Drug Mode of Action
复制标题
DOI：
10.1007/s11390-010-9311-7
复制
发表时间：
2010-01
影响因子：
0.7
通讯作者：
R. Yamaguchi;S. Imoto;S. Miyano
中科院分区：
文献类型：
--
作者：
R. Yamaguchi;S. Imoto;S. Miyano
由于时间进程微阵列数据很短，但包含大量基因，因此大多数统计模型应该进行扩展，以便能够处理这种统计上的不规则情况。我们介绍了生物状态空间模型，这些模型被建立为适合于从微阵列基因表达数据构建基因网络的计算模型。本章阐述了我们的生物状态空间模型的理论和方法，以及一些有代表性的分析，包括发现药物的作用模式。通过应用，我们展示了生物状态空间模型分析的整个策略，包括时程数据的实验设计、模型的建立和估计网络的分析。
Since time-course microarray data are short but contain a large number of genes, most of statistical models should be extended so that they can handle such statistically irregular situations. We introduce biological state space models that are established as suitable computational models for constructing gene networks from microarray gene expression data. This chapter elucidates theory and methodology of our biological state space models together with some representative analyses including discovery of drug mode of action. Through the applications we show the whole strategy of biological state space model analysis involving experimental design of time-course data, model building and analysis of the estimated networks.