Suppr
超能文献

通过 TVNViewer 中的可视化实现动态网络分析。

Enabling dynamic network analysis through visualization in TVNViewer.

机构信息

Joint Carnegie Mellon, University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, PA, USA.

出版信息

BMC Bioinformatics. 2012 Aug 16;13:204. doi: 10.1186/1471-2105-13-204.

DOI:10.1186/1471-2105-13-204

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3447684/

Abstract

BACKGROUND

Many biological processes are context-dependent or temporally specific. As a result, relationships between molecular constituents evolve across time and environments. While cutting-edge machine learning techniques can recover these networks, exploring and interpreting the rewiring behavior is challenging. Information visualization shines in this type of exploratory analysis, motivating the development ofTVNViewer (http://sailing.cs.cmu.edu/tvnviewer), a visualization tool for dynamic network analysis.

RESULTS

In this paper, we demonstrate visualization techniques for dynamic network analysis by using TVNViewer to analyze yeast cell cycle and breast cancer progression datasets.

CONCLUSIONS

TVNViewer is a powerful new visualization tool for the analysis of biological networks that change across time or space.

摘要

背景

许多生物过程是依赖于上下文或具有时间特异性的。因此，分子成分之间的关系随时间和环境而演变。虽然最先进的机器学习技术可以恢复这些网络，但探索和解释这些重新布线的行为具有挑战性。信息可视化在这种探索性分析中大放异彩，这促使了 TVNViewer（http://sailing.cs.cmu.edu/tvnviewer）的开发，这是一种用于动态网络分析的可视化工具。

结果

在本文中，我们通过使用 TVNViewer 来分析酵母细胞周期和乳腺癌进展数据集，展示了动态网络分析的可视化技术。

结论

TVNViewer 是一种强大的新可视化工具，可用于分析随时间或空间变化的生物网络。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59da/3447684/ab1a8470ac36/1471-2105-13-204-1.jpg

相似文献

1

Enabling dynamic network analysis through visualization in TVNViewer.

BMC Bioinformatics. 2012 Aug 16;13:204. doi: 10.1186/1471-2105-13-204.

2

TVNViewer: an interactive visualization tool for exploring networks that change over time or space.

Bioinformatics. 2011 Jul 1;27(13):1880-1. doi: 10.1093/bioinformatics/btr273. Epub 2011 May 5.

3

Knowledge-fused differential dependency network models for detecting significant rewiring in biological networks.

BMC Syst Biol. 2014 Jul 24;8:87. doi: 10.1186/s12918-014-0087-1.

4

KELLER: estimating time-varying interactions between genes.

Bioinformatics. 2009 Jun 15;25(12):i128-36. doi: 10.1093/bioinformatics/btp192.

5

VisANT 3.5: multi-scale network visualization, analysis and inference based on the gene ontology.

Nucleic Acids Res. 2009 Jul;37(Web Server issue):W115-21. doi: 10.1093/nar/gkp406. Epub 2009 May 21.

6

The Local Edge Machine: inference of dynamic models of gene regulation.

Genome Biol. 2016 Oct 19;17(1):214. doi: 10.1186/s13059-016-1076-z.

7

Boolean factor graph model for biological systems: the yeast cell-cycle network.

BMC Bioinformatics. 2021 Sep 17;22(1):442. doi: 10.1186/s12859-021-04361-8.

8

Development and use of a Cytoscape app for GRNCOP2.

Comput Methods Programs Biomed. 2019 Aug;177:211-218. doi: 10.1016/j.cmpb.2019.05.030. Epub 2019 Jun 4.

9

TREEGL: reverse engineering tree-evolving gene networks underlying developing biological lineages.

Bioinformatics. 2011 Jul 1;27(13):i196-204. doi: 10.1093/bioinformatics/btr239.

10

VisANT: an online visualization and analysis tool for biological interaction data.

BMC Bioinformatics. 2004 Feb 19;5:17. doi: 10.1186/1471-2105-5-17.

引用本文的文献

1

Evidence of the digital nomad phenomenon: From "Reinventing" migration theory to destination countries readiness.

Heliyon. 2024 Aug 22;10(17):e36655. doi: 10.1016/j.heliyon.2024.e36655. eCollection 2024 Sep 15.

本文引用的文献

1

TREEGL: reverse engineering tree-evolving gene networks underlying developing biological lineages.

Bioinformatics. 2011 Jul 1;27(13):i196-204. doi: 10.1093/bioinformatics/btr239.

2

TVNViewer: an interactive visualization tool for exploring networks that change over time or space.

Bioinformatics. 2011 Jul 1;27(13):1880-1. doi: 10.1093/bioinformatics/btr273. Epub 2011 May 5.

3

Mental models, visual reasoning and interaction in information visualization: a top-down perspective.

IEEE Trans Vis Comput Graph. 2010 Nov-Dec;16(6):999-1008. doi: 10.1109/TVCG.2010.177.

4

Graphle: Interactive exploration of large, dense graphs.

BMC Bioinformatics. 2009 Dec 14;10:417. doi: 10.1186/1471-2105-10-417.

5

Mapping text with phrase nets.

IEEE Trans Vis Comput Graph. 2009 Nov-Dec;15(6):1169-76. doi: 10.1109/TVCG.2009.165.

6

Molecular networks as sensors and drivers of common human diseases.

Nature. 2009 Sep 10;461(7261):218-23. doi: 10.1038/nature08454.

7

Recovering time-varying networks of dependencies in social and biological studies.

Proc Natl Acad Sci U S A. 2009 Jul 21;106(29):11878-83. doi: 10.1073/pnas.0901910106. Epub 2009 Jul 1.

8

KELLER: estimating time-varying interactions between genes.

Bioinformatics. 2009 Jun 15;25(12):i128-36. doi: 10.1093/bioinformatics/btp192.

9

Grouped graphical Granger modeling for gene expression regulatory networks discovery.

Bioinformatics. 2009 Jun 15;25(12):i110-8. doi: 10.1093/bioinformatics/btp199.

10

VisANT 3.5: multi-scale network visualization, analysis and inference based on the gene ontology.

Nucleic Acids Res. 2009 Jul;37(Web Server issue):W115-21. doi: 10.1093/nar/gkp406. Epub 2009 May 21.

文献AI研究员

20分钟写一篇综述，助力文献阅读效率提升50倍。

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

文档翻译

学术文献翻译模型，支持多种主流文档格式。