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dc.contributor.advisorPedersen, Edvard
dc.contributor.advisorBongo, Lars Ailo
dc.contributor.advisorDumeaux, Vanessa
dc.contributor.authorMartínez Fernández, Álvaro
dc.date.accessioned2021-02-04T14:45:31Z
dc.date.available2021-02-04T14:45:31Z
dc.date.issued2020-11-15
dc.description.abstractBiological data is often visualized using networks. However, these networks face problems such as information overload, high interconnectivity, and high dimensionality. Existing approaches try to solve these problems by reducing the interactivity in favor of presenting more information or by using expensive hardware. This thesis aims to solve them using Virtual Reality (VR) and the Oculus Quest, an affordable VR headset, by taking advantage of the rich interactivity that VR offers. In order to test our hypothesis that Virtual Reality can be advantageous in the visualization of large biological networks, we built GeneNet VR, an open-source prototype of a VR application for the Oculus Quest for the interactive visualization of large biological networks. As a case study, we used two gene networks from MIxT, a real application that uses a 2-dimensional network visualization. We evaluated the performance and scalability of GeneNet VR and we conducted in-depth semi-structured interviews with several research scientists to evaluate the usability of our approach. Our result shows that the performance of the interactions for network visualization on a machine, reaches the 72 FPS required by the Oculus’ performance guidelines and that GeneNet VR scales for our largest network with 2693 nodes. We also evaluated the performance of GeneNet VR on the Oculus Quest hardware, which also achieved 72 FPS. The Oculus Quest is therefore an affordable option for the visualization of large datasets. From the interviews, we learned that GeneNet VR is an innovative and interesting visualization tool for large biological networks and that is easy to use even for novice VR users. Thus, VR hardware like the Oculus Quest should be considered a competitive solution for visualization tools, as described in this thesis. GeneNet VR is open-source and can be accessed with the following link: <a href=https://github.com/kolibrid/GeneNet-VR>https://github.com/kolibrid/GeneNet-VR</a>. We created also a video to show the different interactions that we can do with GeneNet VR to explore large biological networks: <a href=https://youtu.be/N4QDZiZqVNY>https://youtu.be/N4QDZiZqVNY</a>.en_US
dc.identifier.urihttps://hdl.handle.net/10037/20518
dc.language.isoengen_US
dc.publisherUiT Norges arktiske universiteten_US
dc.publisherUiT The Arctic University of Norwayen_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2020 The Author(s)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/4.0en_US
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)en_US
dc.subject.courseIDINF-3990
dc.subjectVDP::Technology: 500::Information and communication technology: 550en_US
dc.subjectVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550en_US
dc.subjectVDP::Technology: 500::Information and communication technology: 550::Computer technology: 551en_US
dc.subjectVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550::Datateknologi: 551en_US
dc.subjectVDP::Mathematics and natural science: 400::Basic biosciences: 470::Bioinformatics: 475en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Basale biofag: 470::Bioinformatikk: 475en_US
dc.titleGeneNet VR: Large Biological Networks in Virtual Reality Using Inexpensive Hardwareen_US
dc.typeMaster thesisen_US
dc.typeMastergradsoppgaveen_US


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Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
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