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dc.contributor.authorKuznetsova, I.
dc.contributor.authorLugmayr, Artur
dc.contributor.authorHolzinger, A.
dc.date.accessioned2018-08-08T04:41:36Z
dc.date.available2018-08-08T04:41:36Z
dc.date.created2018-08-08T03:50:40Z
dc.date.issued2017
dc.identifier.citationKuznetsova, I. and Lugmayr, A. and Holzinger, A. 2017. Visualisation Methods of Hierarchical Biological Data: A Survey and Review, Artificial Intelligence MEETS Virtual and Augmented Worlds (AIVR), Proceedings of the 10th International Workshop on Semantic Ambient Media Experiences (SAME 2018), in Conjunction with SIGGRAPH Asia, pp. 32-39: International Ambient Media Association (iAMEA).
dc.identifier.urihttp://hdl.handle.net/20.500.11937/69607
dc.description.abstract

The sheer amount of high dimensional biomedical data requires machine learning, and advanced data visualization techniques to make the data understandable for human experts. Most biomedical data today is in arbitrary high dimensional spaces, and is not directly accessible to the human expert for a visual and interactive analysis process. To cope with this challenge, the application of machine learning and knowledge extraction methods is indispensable throughout the entire data analysis workflow. Nevertheless, human experts need to understand and interpret the data and experimental results. Appropriate understanding is typically supported by visualizing the results adequately, which is not a simple task. Consequently, data visualization is one of the most crucial steps in conveying biomedical results. It can and should be considered as a critical part of the analysis pipeline. Still as of today, 2D representations dominate, and human perception is limited to this lower dimension to understand the data. This makes the visualization of the results in an understandable and comprehensive manner a grand challenge. This paper reviews the current state of visualization methods in a biomedical context. It focuses on hierarchical biological data as a source for visualization, and gives a comprehensive

dc.publisherInternational Ambient Media Association (iAMEA)
dc.relation.urihttp://www.ambientmediaassociation.org/Journal
dc.titleVisualisation Methods of Hierarchical Biological Data: A Survey and Review
dc.typeConference Paper
dcterms.source.volume2
dcterms.source.startPage32
dcterms.source.endPage39
dcterms.source.isbn978-952-7023-17-4
dcterms.source.conferenceArtificial Intelligence MEETS Virtual and Augmented Worlds (AIVR), Proceedings of the 10th International Workshop on Semantic Ambient Media Experiences (SAME 2018), in Conjunction with SIGGRAPH Asia
dcterms.source.placeBankok, Thailand
curtin.departmentSchool of Media, Creative Arts and Social Inquiry (MCASI)
curtin.accessStatusFulltext not available


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