ArticleBMC bioinformatics2014
Analysis of biomedical data with multilevel glyphs.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 37 citations in OpenAlex.
- Establishment of a machine learning-based prediction framework to assess trade-offs in decisions that affect post-HCT outcomes.Computers in biology and medicine · 2025Article
- ShapoGraphy: A User-Friendly Web Application for Creating Bespoke and Intuitive Visualisation of Biomedical Data.Frontiers in bioinformatics · 2022Article
- Sensors and Functionalities of Non-Invasive Wrist-Wearable Devices: A Review.Sensors (Basel, Switzerland) · 2018Review
- Personalized medicine-a modern approach for the diagnosis and management of hypertension.Clinical science (London, England : 1979) · 2017Review
- Automatic classification of histopathological diagnoses for building a large scale tissue catalogue.Health and technology · 2017Article
- Precision medicine in cardiology.Nature reviews. Cardiology · 2016Review
- Interactive knowledge discovery with the doctor-in-the-loop: a practical example of cerebral aneurysms research.Brain informatics · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis 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.
methodsIn 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.
resultsWe 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.
conclusionsData 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.