The power of network visualizations

scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

Header Abstract Content Author and article information References Comments

Record: found
Abstract: found
Conference Proceedings: found

Is Open Access

The power of network visualizations

Published

proceedings-article

Author(s): Jörg Menche ¹

Publication date (Electronic): 30 August 2022

Conference name: RExPO22

Conference date: 2-3 September, 2022

Keywords: Biological networks, Network visualizations, Virtual Reality

Bookmark

Abstract

Virtually all processes in health and disease rely on the careful orchestration of a large number of diverse individual components ranging from molecules to cells and entire organs. Networks provide a powerful framework for describing and understanding these complex systems in a holistic fashion [1]. In addition to a plethora of mathematical and computational tools, networks also offer a uniquely intuitive visual interface to big data. However, a critical roadblock for meaningful network visualizations, in particular for larger, more complex networks, is the lack of layouts that allow for a straightforward interpretation of observed visual patterns. Indeed, standard layout algorithms are prone to generating proverbial ‘hairball’ visualizations that often obscure network structure, rather than elucidate it. As a result, it is often not possible to visually recognize connection patterns that can be analytically proven to be present in a particular network, or, conversely, to trust visual patterns to truly reflect an intrinsic structural feature of a network. In my presentation I will first introduce our recent work on network cartographs for creating interpretable network layouts [2]. Network cartographs use dimensionality reduction techniques to map network information directly into node positions. Any network information can be encoded and visualized in this fashion, including internal information, such as the structural importance of a particular node within the network, but also external node annotations, such as the similarity of nodes with respect to a given functional characteristic. The flexibility of our framework allows users to custom-tailor network visualizations for a specific application. For example, we designed a 3D interactome layout specifically for inspecting the biological functions of candidate genes that are suspected to cause rare diseases [3]. Finally, I will show how network layouts can be incorporated into our recently developed Virtual Reality (VR) network exploration platform VRNetzer [4]. The VRNetzer platform represents a general purpose, VR-based data exploration platform for large and diverse data types by providing an interface that facilitates the interaction between human intuition and state-of-the-art analysis methods. Its design allows for maximal customization and extendibility, through the import of custom code for data analysis, integration of external databases, and design of arbitrary user interface elements, among other features.

Content

Author and article information

Conference

Publication date (Electronic): 30 August 2022

Affiliations

[1 ] Department of Structural and Computational Biology, Max Perutz Labs, University of Vienna, Vienna (Austria)

Article

DOI: 10.14293/S2199-rexpo22014.v1

SO-VID: 085861a6-2b11-462d-a1fd-44bd80331d2f

License:

Published under Creative Commons Attribution 4.0 International ( CC BY 4.0). Users are allowed to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material for any purpose, even commercially), as long as the authors and the publisher are explicitly identified and properly acknowledged as the original source.

Conference name: RExPO22

Conference location: Maastricht, Netherlands

Conference date: 2-3 September, 2022

History

Product

ScienceOpen

Data availability: Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

ScienceOpen disciplines: Quantitative & Systems biology,Molecular medicine,Bioinformatics & Computational biology,Pharmacology & Pharmaceutical medicine

Keywords: Biological networks,Network visualizations,Virtual Reality

References

Caldera Michael, Buphamalai Pisanu, Müller Felix, Menche Jörg. Interactome-based approaches to human disease. Current Opinion in Systems Biology. Vol. 3:88–94. 2017. Elsevier BV. [Cross Ref]
Hütter Christiane V. R., Sin Celine, Müller Felix, Menche Jörg. Network cartographs for interpretable visualizations. Nature Computational Science. Vol. 2(2):84–89. 2022. Springer Science and Business Media LLC. [Cross Ref]
Buphamalai Pisanu, Kokotovic Tomislav, Nagy Vanja, Menche Jörg. Network analysis reveals rare disease signatures across multiple levels of biological organization. Nature Communications. Vol. 12(1)2021. Springer Science and Business Media LLC. [Cross Ref]
Pirch Sebastian, Müller Felix, Iofinova Eugenia, Pazmandi Julia, Hütter Christiane V. R., Chiettini Martin, Sin Celine, Boztug Kaan, Podkosova Iana, Kaufmann Hannes, Menche Jörg. The VRNetzer platform enables interactive network analysis in Virtual Reality. Nature Communications. Vol. 12(1)2021. Springer Science and Business Media LLC. [Cross Ref]

Comments

Comment on this article

[1] Caldera Michael, Buphamalai Pisanu, Müller Felix, Menche Jörg. Interactome-based approaches to human disease. Current Opinion in Systems Biology. Vol. 3:88–94. 2017. Elsevier BV. [Cross Ref]

[2] Hütter Christiane V. R., Sin Celine, Müller Felix, Menche Jörg. Network cartographs for interpretable visualizations. Nature Computational Science. Vol. 2(2):84–89. 2022. Springer Science and Business Media LLC. [Cross Ref]

[3] Buphamalai Pisanu, Kokotovic Tomislav, Nagy Vanja, Menche Jörg. Network analysis reveals rare disease signatures across multiple levels of biological organization. Nature Communications. Vol. 12(1)2021. Springer Science and Business Media LLC. [Cross Ref]

[4] Pirch Sebastian, Müller Felix, Iofinova Eugenia, Pazmandi Julia, Hütter Christiane V. R., Chiettini Martin, Sin Celine, Boztug Kaan, Podkosova Iana, Kaufmann Hannes, Menche Jörg. The VRNetzer platform enables interactive network analysis in Virtual Reality. Nature Communications. Vol. 12(1)2021. Springer Science and Business Media LLC. [Cross Ref]