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迈向细胞的可缩放多维图谱。

Towards zoomable multidimensional maps of the cell.

作者信息

Hu Zhenjun, Mellor Joe, Wu Jie, Kanehisa Minoru, Stuart Joshua M, DeLisi Charles

机构信息

Program in Bioinformatics and Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215, USA.

出版信息

Nat Biotechnol. 2007 May;25(5):547-54. doi: 10.1038/nbt1304.

Abstract

The detailed structure of molecular networks, including their dependence on conditions and time, are now routinely assayed by various experimental techniques. Visualization is a vital aid in integrating and interpreting such data. We describe emerging approaches for representing and visualizing systems data and for achieving semantic zooming, or changes in information density concordant with scale. A central challenge is to move beyond the display of a static network to visualizations of networks as a function of time, space and cell state, which capture the adaptability of the cell. We consider approaches for representing the role of protein complexes in the cell cycle, displaying modules of metabolism in a hierarchical format, integrating experimental interaction data with structured vocabularies such as Gene Ontology categories and representing conserved interactions among orthologous groups of genes.

摘要

分子网络的详细结构,包括它们对条件和时间的依赖性,现在通常通过各种实验技术进行测定。可视化是整合和解释此类数据的重要辅助手段。我们描述了用于表示和可视化系统数据以及实现语义缩放(即信息密度随比例变化)的新兴方法。一个核心挑战是超越静态网络的显示,转向将网络可视化为时间、空间和细胞状态的函数,以捕捉细胞的适应性。我们考虑了表示蛋白质复合物在细胞周期中的作用、以分层格式显示代谢模块、将实验性相互作用数据与诸如基因本体类别等结构化词汇整合以及表示直系同源基因群之间保守相互作用的方法。

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