Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz

Logo MUG-Forschungsportal

Gewählte Publikation:

SHR Neuro Krebs Kardio Lipid Stoffw Microb

Müller, H; Reihs, R; Zatloukal, K; Holzinger, A.
Analysis of biomedical data with multilevel glyphs.
BMC Bioinformatics. 2014; 15 Suppl 6(6):S5-S5 Doi: 10.1186/1471-2105-15-S6-S5 [OPEN ACCESS]
Web of Science PubMed PUBMED Central FullText FullText_MUG

 

Führende Autor*innen der Med Uni Graz
Müller Heimo
Co-Autor*innen der Med Uni Graz
Holzinger Andreas
Reihs Robert
Zatloukal Kurt
Altmetrics:

Dimensions Citations:

Plum Analytics:

Scite (citation analytics):

Abstract:
This paper presents multilevel data glyphs optimized for the interactive knowledge discovery and visualization of large biomedical data sets. Data glyphs are three- dimensional objects defined by multiple levels of geometric descriptions (levels of detail) combined with a mapping of data attributes to graphical elements and methods, which specify their spatial position. In the data mapping phase, which is done by a biomedical expert, meta information about the data attributes (scale, number of distinct values) are compared with the visual capabilities of the graphical elements in order to give a feedback to the user about the correctness of the variable mapping. The spatial arrangement of glyphs is done in a dimetric view, which leads to high data density, a simplified 3D navigation and avoids perspective distortion. We show the usage of data glyphs in the disease analyser a visual analytics application for personalized medicine and provide an outlook to a biomedical web visualization scenario. Data glyphs can be successfully applied in the disease analyser for the analysis of big medical data sets. Especially the automatic validation of the data mapping, selection of subgroups within histograms and the visual comparison of the value distributions were seen by experts as an important functionality.
Find related publications in this database (using NLM MeSH Indexing)
Data Mining -
Humans -
Internet -
Medical Informatics - instrumentation
Medical Informatics - methods

© Med Uni Graz Impressum