BioNetIllustration: User Centric Illustrations of Biological Networks

In living systems, one molecule is commonly involved in several distinct physiological functions. The roles of molecules are commonly summarized in pathway diagrams, which, however, are abstract, hierarchically nested and thus is difficult to comprehend especially by non-expert audience. The primary goal of this research in visualization is to intuitively support the comprehensive understanding of relationships among biological networks using interactively computed illustrations. Illustrations, especially in textbooks of biology are carefully designed to clearly present reactions between organs as well as interactions within cells. Automatic generation of illustrative visualizations of biological networks is thus the technical content of this proposal. Automatic generation of hand-drawn illustrations has been a challenging task due to the difficulty of algorithmically describing a human creative process such as evaluating and selecting significant information and composing meaningful explanations in a visually plausible manner. The project also involves experts from several disciplines including network and medical visualization, data mining, systems biology as well as perceptual psychology. The result will provide a new direction for physiological process analysis and accelerate the knowledge transfer not only within experts but also to the public. Acknowledgment: The project has received funding from the European Union Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 747985.


  • Horizon 2020 Marie Sklodowska-Curie Actions (MSCA) 747985

Research Areas

Research Area
Information Visualization and Visual Analytics
In this research area, our focus lies on novel visual encodings and interaction techniques to explore a large amount of abstract data, often in combination with analytical reasoning.
Biological Data Visualization
In this research area, we develop new visualization techniques to support biologists in data analysis and create visualizations to disseminate scientific discoveries in biology.


18 Publications found:
Image Bib Reference Publication Type
Hsiang-Yun Wu, Martin Nöllenburg, Ivan Viola
Multi-level Area Balancing of Clustered Graphs
IEEE Transactions on Visualization and Computer Graphics (TVCG), x:1-15, November 2020. [paper] [video]
Journal Paper (without talk)
Ladislav Čmolík, Václav Pavlovec, Hsiang-Yun Wu, Martin Nöllenburg
Mixed Labeling: Integrating Internal and External Labels
IEEE Transactions on Visualization and Computer Graphics (TVCG), x:1-14, September 2020. [image] [paper]
Journal Paper (without talk)
Helen C. Purchase, Daniel Archambault, Stephen Kobourov, Martin Nöllenburg, Sergey Pupyrev, Hsiang-Yun Wu
The Turing Test for Graph Drawing Algorithms
In Proceedings of the 28th International Symposium on Graph Drawing and Network Visualization (GD2020), pages 1-16. September 2020.
[image] [paper]
Conference Paper
Hsiang-Yun Wu, Martin Nöllenburg, Ivan Viola
Graph Models for Biological Pathway Visualization: State of the Art and Future Challenges, 20. October 2019, Vis 2019 Workshop, Canada
Susanne Rinortner
Visualizing Protein Interactions in Corresponding Compartments
Bachelor Thesis
Maximillian Sbardellati, Haichao Miao, Hsiang-Yun Wu, Meister Eduard Gröller, Ivan Barisic, Ivan Viola
Interactive Exploded Views for Molecular Structures
In Proceedings of the 9th Eurographics Workshop on Visual Computing for Biology and Medicine, pages 103-112. September 2019.
Conference Paper
Hsiang-Yun Wu, Martin Nöllenburg, Ivan Viola
Map of Metabolic Harmony
Unknown Publication
Kazuyo Mizuno, Hsiang-Yun Wu, Shigeo Takahashi, Takeo Igarashi
Optimizing Stepwise Animation in Dynamic Set Diagrams
Computer Graphics Forum, 38:13-24, July 2019. [paper] [video]
Journal Paper with Conference Talk
Hsiang-Yun Wu, Martin Nöllenburg, Filipa L. Sousa, Ivan Viola
Metabopolis: Scalable Network Layout for Biological Pathway Diagrams in Urban Map Style
BMC Bioinformatics, 20(187):1-20, May 2019. [paper] [video]
Journal Paper (without talk)
Hsiang-Yun Wu, Benjamin Niedermann, Shigeo Takahashi, Martin Nöllenburg
A Survey on Computing Schematic Network Maps: The Challenge to Interactivity, 11. April 2019, The 2nd Schematic Mapping Workshop 2019, Vienna, Austria
Hsiang-Yun Wu, Haichao Miao, Ivan Viola
From Cells to Atoms - Biological Information Visualization (in Chinese)
TR-193-02-2019-1, March 2019 [paper]
Technical Report
Vahan Yoghourdjian, Daniel Archambault, Stephan Diehl, Tim Dwyer, Karsten Klein, Helen C. Purchase, Hsiang-Yun Wu
Exploring the limits of complexity: A survey of empirical studies ongraph visualisation
Visual Informatics, 2(4):264-282, January 2019. [paper]
Journal Paper (without talk)
David Kouřil, Ladislav Čmolík, Barbora Kozlikova, Hsiang-Yun Wu, Graham Johnson, David Goodsell, Arthur Olson, Meister Eduard Gröller, Ivan Viola
Labels on Levels: Labeling of Multi-Scale Multi-Instance and Crowded 3D Biological Environments
IEEE Transactions on Visualization and Computer Graphics, 25:977-986, January 2019. [LoL-conference-presentation] [paper] [Conference Talk Recording]
Journal Paper with Conference Talk
Daniel Archambault, Jessie Kennedy, Tatiana von Landesberger, Mark McCann, Fintan McGee, Benjamin Renoust, Hsiang-Yun Wu
Lost in Translation: Alignment of Mental Representations for Visual Analytics, Reimagining the Mental Map and Drawing Stability (NII Shonan Meeting Seminar 127)
TR-193-02-2018-1, December 2018 [paper]
Technical Report
Hsiang-Yun Wu, Martin Nöllenburg, Ivan Viola
A Visual Comparison of Hand-Drawn and Machine-Generated Human Metabolic Pathways
Poster shown at EuroVis ( 4. June 2018- 8. June 2018)
Hsiang-Yun Wu, Martin Nöllenburg, Ivan Viola
The Travel of a Metabolite
submitted to PacificVis 2018 Data Story Telling Contest
[paper] [video]
Miscellaneous Publication
Hsiang-Yun Wu, Shigeo Takahashi, Rie Ishida
Overlap-Free Labeling of Clustered Networks Based on Voronoi Tessellation
Journal of Visual Languages & Computing, (44), February 2018. [paper]
Journal Paper (without talk)
Radu Jianu, Martin Krzywinski, Luana Micallef, Hsiang-Yun Wu
Mapifying the Genome, Scalable Set Visualizations (Dagstuhl Seminar 17332)
TR-193-02-2018-2, 2018 [paper]
Technical Report
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