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  • Autor
    • Wartner, Sandra
    • Girardi, Dominic
    • Trenkler, Johannes
    • Wiesinger-Widi, Manuela
    • Kleiser, Raimund
    • Holzinger, Andreas
  • TitelOntology-Guided Principal Component Analysis: Reaching the limits of the doctor-in-the-loop
  • Datei
  • Persistent Identifier
  • Erscheinungsjahr2016
  • Seiten22-33
  • LicenceCC-BY
  • ZugriffsrechteCC-BY
  • Download Statistik457
  • Peer ReviewJa
  • AbstractBiomedical research requires deep domain expertise to perform analyses of complex data sets, assisted by mathematical expertise provided by data scientists who design and develop sophisticated methods and tools. Such methods and tools not only require preprocessing of the data, but most of all a meaningful input selection. Usually, data scientists do not have sufficient background knowledge about the origin of the data and the biomedical problems to be solved, consequently a doctor-in-the-loop can be of great help here. In this paper we revise the viability of integrating an analysis guided visualization component in an ontology-guided data infrastructure, exemplified by the principal component analysis. We evaluated this approach by examining the potential for intelligent support of medical experts on the case of cerebral aneurysms research.