Systems analysis of high-throughput data.

Systems analysis of high-throughput data.
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高通量数据的系统分析。

DOI:
10.1007/978-1-4939-2095-2_8
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发表时间:
2014
影响因子:
--
通讯作者:
Braun, Rosemary
Braun, Rosemary
中科院分区:
医学4区
文献类型:
--
作者:
Braun, Rosemary

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现代高通量分析产生生物样品的基因组、转录组和蛋白质组状态的详细表征,使我们能够探测调节造血或引起血液疾病的分子机制。与此同时,数据的高维性和生物相互作用网络的复杂性在识别因果变化和对底层系统生物学建模方面提出了重大的分析挑战。除了确定显着失调的基因和蛋白质,整合分析方法,使这些单一的基因在功能范围内的调查是必要的。本章提出了一个调查目前的计算方法的统计分析的高维数据和系统水平的细胞信号和调节模型的发展。具体来说,我们专注于多基因分析方法和表达数据与领域知识(如生物学途径)和其他基因信息(例如,序列或甲基化数据)来鉴定复杂细胞相互作用网络中的新功能模块。
Modern high–throughput assays yield detailed characterizations of the genomic, transcriptomic, and proteomic states of biological samples, enabling us to probe the molecular mechanisms that regulate hematopoiesis or give rise to hematological disorders. At the same time, the high dimensionality of the data and the complex nature of biological interaction networks present significant analytical challenges in identifying causal variations and modeling the underlying systems biology. In addition to identifying significantly disregulated genes and proteins, integrative analysis approaches that allow the investigation of these single genes within a functional context are required. This chapter presents a survey of current computational approaches for the statistical analysis of high–dimensional data and the development of systems–level models of cellular signaling and regulation. Specifically, we focus on multi–gene analysis methods and the integration of expression data with domain knowledge (such as biological pathways) and other gene–wise information (e.g., sequence or methylation data) to identify novel functional modules in the complex cellular interaction network.
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