An Efficient Approach Towards the Generation and Analysis of Interoperable Clinical Data in a Knowledge Graph

• Autorzy: Dörpinghaus Jens , Weil Vera , Schaaf Sebastian , Hübenthal Tobias

Identyfikatory

DOI

10.15439/2021F29

• Konferencja: Federated Conference on Computer Science and Information Systems (16 ; 02-05.09.2021 ; online)
• Języki publikacji: EN

Abstrakty

EN

Knowledge graphs have been shown to play an important role in recent knowledge mining and discovery, for example in the field of life sciences or bioinformatics. Contextual information is widely used for NLP and knowledge discovery in life sciences since it highly influences the exact meaning of natural language and also queries for data. The contributions of this paper are (1) an efficient approach towards interoperable data, (2) a runtime analysis of 14 real world use cases represented by graph queries and (3) a unique view on clinical data and its application combining methods of algorithmic optimisation, graph theory and data science.

Słowa kluczowe

EN

bioinformatics; data mining; graph theory

PL

bioinformatyka; eksploracja danych; teoria grafów

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2021
• Tom: Vol. 25
• Strony: 59--68
• Opis fizyczny: Bibliogr. 30 poz., rys., tab., wykr.

Twórcy

autor

Dörpinghaus Jens

• German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany

autor

Weil Vera

• Department of Mathematics and Computer Science, University of Cologne, Germany

autor

Schaaf Sebastian

• Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, Sankt Augustin, Germany

autor

Hübenthal Tobias

• Department of Mathematics and Computer Science, University of Cologne, Germany

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Uwagi

1. Track 1: Artificial Intelligence in Applications

2. Session: 15th International Symposium Advances in Artificial Intelligence and Applications