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dc.contributor.authorWu, Hsiang-Yun
dc.contributor.authorNöllenburg, Martin
dc.contributor.authorViola, Ivan
dc.date.accessioned2021-10-13T06:57:12Z
dc.date.available2021-10-13T06:57:12Z
dc.date.issued2021-10-10
dc.identifier.urihttp://hdl.handle.net/10754/672819
dc.description.abstractThe concept of multilayer networks has become recently integrated into complex systems modeling since it encapsulates a very general concept of complex relationships. Biological pathways are an example of complex real-world networks, where vertices represent biological entities, and edges indicate the underlying connectivity. For this reason, using multilayer networks to model biological knowledge allows us to formally cover essential properties and theories in the field, which also raises challenges in visualization. This is because, in the early days of pathway visualization research, only restricted types of graphs, such as simple graphs, clustered graphs, and others were adopted. In this paper, we revisit a heterogeneous definition of biological networks and aim to provide an overview to see the gaps between data modeling and visual representation. The contribution will, therefore, lie in providing guidelines and challenges of using multilayer networks as a unified data structure for the biological pathway visualization.
dc.description.sponsorshipThe project has received funding from the EU Horizon 2020 research and innovation programme under the MSCA grant No. 747985, from the Vienna Science and Technology Fund (WWTF) grant No. VRG11-010, from the Austrian Science Fund (FWF) grant No. P31119, and from King Abdullah University of Science and Technology (KAUST) through award BAS/1/1680-01-01.
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/2110.04808.pdf
dc.rightsArchived with thanks to arXiv
dc.titleGraph Models for Biological Pathway Visualization: State of the Art and Future Challenges
dc.typePreprint
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.contributor.departmentVisual Computing Center (VCC)
dc.eprint.versionPre-print
dc.contributor.institutionTU Wien, Austria
dc.identifier.arxivid2110.04808
kaust.personViola, Ivan
kaust.grant.numberBAS/1/1680-01-01
refterms.dateFOA2021-10-13T07:01:16Z
kaust.acknowledged.supportUnitBAS


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