Zitieren Sie bitte immer diesen URN: urn:nbn:de:kobv:b43-580858
The importance of graph databases and graph learning for clinical applications
- The increasing amount and complexity of clinical data require an appropriate way of storing and analyzing those data. Traditional approaches use a tabular structure (relational databases) for storing data and thereby complicate storing and retrieving interlinked data from the clinical domain. Graph databases provide a great solution for this by storing data in a graph as nodes (vertices) that are connected by edges (links). The underlying graph structure can be used for the subsequent data analysis (graph learning). Graph learning consists of two parts: graph representation learning and graph analytics. Graph representation learning aims to reduce high-dimensional input graphs to low-dimensional representations. Then, graph analytics uses the obtained representations for analytical tasks like visualization, classification, link prediction and clustering which can be used to solve domain-specific problems. In this survey, we review current state-of-the-art graph database managementThe increasing amount and complexity of clinical data require an appropriate way of storing and analyzing those data. Traditional approaches use a tabular structure (relational databases) for storing data and thereby complicate storing and retrieving interlinked data from the clinical domain. Graph databases provide a great solution for this by storing data in a graph as nodes (vertices) that are connected by edges (links). The underlying graph structure can be used for the subsequent data analysis (graph learning). Graph learning consists of two parts: graph representation learning and graph analytics. Graph representation learning aims to reduce high-dimensional input graphs to low-dimensional representations. Then, graph analytics uses the obtained representations for analytical tasks like visualization, classification, link prediction and clustering which can be used to solve domain-specific problems. In this survey, we review current state-of-the-art graph database management systems, graph learning algorithms and a variety of graph applications in the clinical domain. Furthermore, we provide a comprehensive use case for a clearer understanding of complex graph learning algorithms.…
Autor*innen: | D. Walke, D. Micheel, K. Schallert, Thilo MuthORCiD, D. Broneske, G. Saake, R. Heyer |
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Dokumenttyp: | Zeitschriftenartikel |
Veröffentlichungsform: | Verlagsliteratur |
Sprache: | Englisch |
Titel des übergeordneten Werkes (Englisch): | Database |
Jahr der Erstveröffentlichung: | 2023 |
Organisationseinheit der BAM: | VP Vizepräsident |
VP Vizepräsident / VP.1 eScience | |
Veröffentlichende Institution: | Bundesanstalt für Materialforschung und -prüfung (BAM) |
Erste Seite: | 1 |
Letzte Seite: | 20 |
DDC-Klassifikation: | Naturwissenschaften und Mathematik / Chemie / Analytische Chemie |
Freie Schlagwörter: | Graph databases; Graph learning; RDF; Review |
Themenfelder/Aktivitätsfelder der BAM: | Chemie und Prozesstechnik |
DOI: | 10.1093/database/baad045 |
URN: | urn:nbn:de:kobv:b43-580858 |
ISSN: | 1758-0463 |
Verfügbarkeit des Dokuments: | Datei für die Öffentlichkeit verfügbar ("Open Access") |
Lizenz (Deutsch): | Creative Commons - CC BY - Namensnennung 4.0 International |
Datum der Freischaltung: | 17.08.2023 |
Referierte Publikation: | Ja |
Datum der Eintragung als referierte Publikation: | 17.08.2023 |
Schriftenreihen ohne Nummerierung: | Wissenschaftliche Artikel der BAM |