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Minimum dominating set-based methods for analyzing biological networks

机译:基于最小支配集的生物网络分析方法

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The fast increase of 'multi-omics' data does not only pose a computational challenge for its analysis but also requires novel algorithmic methodologies to identify complex biological patterns and decipher the ultimate roots of human disorders. To that end, the massive integration of omics data with disease phenotypes is offering a new window into the cell functionality. The minimum dominating set (MDS) approach has rapidly emerged as a promising algorithmic method to analyze complex biological networks integrated with human disorders, which can be composed of a variety of omics data, from proteomics and transcriptomics to metabolomics. Here we review the main theoretical foundations of the methodology and the key algorithms, and examine the recent applications in which biological systems are analyzed by using the MDS approach. (C) 2016 Elsevier Inc. All rights reserved.
机译:“多组学”数据的快速增长不仅对其分析提出了计算挑战,而且还需要新颖的算法方法来识别复杂的生物学模式并破译人类疾病的最终根源。为此,组学数据与疾病表型的大规模整合为细胞功能提供了新的窗口。最小支配集(MDS)方法已迅速成为一种有前途的算法方法,用于分析与人类疾病相集成的复杂生物网络,该网络可以由蛋白质组学,转录组学和代谢组学等多种组学数据组成。在这里,我们回顾了该方法的主要理论基础和关键算法,并研究了使用MDS方法分析生物系统的最新应用。 (C)2016 Elsevier Inc.保留所有权利。

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