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    Visualisation Methods of Hierarchical Biological Data: A Survey and Review

    Access Status
    Fulltext not available
    Authors
    Kuznetsova, I.
    Lugmayr, Artur
    Holzinger, A.
    Date
    2017
    Type
    Conference Paper
    
    Metadata
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    Citation
    Kuznetsova, 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).
    Source Conference
    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
    Additional URLs
    http://www.ambientmediaassociation.org/Journal
    ISBN
    978-952-7023-17-4
    School
    School of Media, Creative Arts and Social Inquiry (MCASI)
    URI
    http://hdl.handle.net/20.500.11937/69607
    Collection
    • Curtin Research Publications
    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

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