The VRNetzer platform enables interactive network analysis in Virtual Reality

Information

• Publication Type: Journal Paper (without talk)
• Workgroup(s)/Project(s):
  • Virtual Reality Group
• Date: April 2021
• DOI: 10.1038/s41467-021-22570-w
• Journal: Nature Communications
• Number: 2432
• Open Access: yes
• Volume: 12
• Pages: 1 – 14
• Keywords: virtual realitz

Abstract

Networks provide a powerful representation of interacting components within complex systems, making them ideal for visually and analytically exploring big data. However, the size and complexity of many networks render static visualizations on typically-sized paper or screens impractical, resulting in proverbial ‘hairballs’. Here, we introduce a Virtual Reality (VR) platform that overcomes these limitations by facilitating the thorough visual, and interactive, exploration of large networks. Our platform allows 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. As a proof of concept, we show how our platform can be used to interactively explore genome-scale molecular networks to identify genes associated with rare diseases and understand how they might contribute to disease development. Our platform represents a general purpose, VRbased 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.

Additional Files and Images

Weblinks

• Publication
  The text of the publicatoin (open access).
• Entry in reposiTUm (TU Wien Publication Database)
• Entry in the publication database of TU-Wien
• DOI: 10.1038/s41467-021-22570-w

BibTeX

@article{pirch_2021_VRN,
  title =      "The VRNetzer platform enables interactive network analysis
               in Virtual Reality",
  author =     "Sebastian Pirch and Felix M\"{u}ller and Eugenia Iofinova
               and Julia Pazmandi and Christiane  H\"{u}tter and Martin
               Chiettini and Celine Sin and Kaan Boztug and Iana Podkosova
               and Hannes Kaufmann and J\"{o}rg Menche",
  year =       "2021",
  abstract =   "Networks provide a powerful representation of interacting
               components within complex systems, making them ideal for
               visually and analytically exploring big data. However, the
               size and complexity of many networks render static
               visualizations on typically-sized paper or screens
               impractical, resulting in proverbial ‘hairballs’. Here,
               we introduce a Virtual Reality (VR) platform that overcomes
               these limitations by facilitating the thorough visual, and
               interactive, exploration of large networks. Our platform
               allows 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. As a proof of concept, we
               show how our platform can be used to interactively explore
               genome-scale molecular networks to identify genes associated
               with rare diseases and understand how they might contribute
               to disease development. Our platform represents a general
               purpose, VRbased 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.",
  month =      apr,
  doi =        "10.1038/s41467-021-22570-w",
  journal =    "Nature Communications",
  number =     "2432",
  volume =     "12",
  pages =      "1--14",
  keywords =   "virtual realitz",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2021/pirch_2021_VRN/",
}