Graph-based methods for analysing networks in cell biology
Graph-based methods for analysing networks in cell biology
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DOI：
10.1093/bib/bbl022
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发表时间：
2006-09-01
影响因子：
9.5
通讯作者：
Schwikowski, Benno
中科院分区：
文献类型：
--
作者：
Aittokallio, Tero;Schwikowski, Benno
细胞生物学的大规模实验数据的可用性使计算方法能够系统地模拟细胞网络的行为。本文综述了图驱动方法在复杂细胞网络分析领域的最新进展。这些方法概述了三个层次的日益复杂的，从方法，可以表征全球或局部的网络结构特性的方法，可以检测组的互连节点，称为图案或集群，可能涉及共同的基本生物功能。我们还简要总结了最近的方法，数据集成和网络推理，通过基于图的形式主义。最后，我们强调了该领域的一些挑战，并提供了我们个人对大规模数据集基于图的分析的主要未来趋势和发展的看法。
Availability of large-scale experimental data for cell biology is enabling computational methods to systematically model the behaviour of cellular networks. This review surveys the recent advances in the field of graph-driven methods for analysing complex cellular networks. The methods are outlined on three levels of increasing complexity, ranging from methods that can characterize global or local structural properties of networks to methods that can detect groups of interconnected nodes, called motifs or clusters, potentially involved in common elementary biological functions. We also briefly summarize recent approaches to data integration and network inference through graph-based formalisms. Finally, we highlight some challenges in the field and offer our personal view of the key future trends and developments in graph-based analysis of large-scale datasets.