• The importance of graph d...

Treffer: The importance of graph databases and graph learning for clinical applications.

Title:
The importance of graph databases and graph learning for clinical applications.
Authors:
Walke D; Bioprocess Engineering, Otto von Guericke University, Universitätsplatz 2, Magdeburg 39106, Germany.; Database and Software Engineering Group, Otto von Guericke University, Universitätsplatz 2, Magdeburg 39106, Germany., Micheel D; Database and Software Engineering Group, Otto von Guericke University, Universitätsplatz 2, Magdeburg 39106, Germany., Schallert K; Multidimensional Omics Analyses Group, Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Bunsen-Kirchhoff-Straße 11, Dortmund 44139, Germany., Muth T; Section eScience (S.3), Federal Institute for Materials Research and Testing (BAM), Unter den Eichen 87, Berlin 12205, Germany., Broneske D; Infrastructure and Methods, German Center for Higher Education Research and Science Studies (DZHW), Lange Laube 12, Hannover 30159, Germany., Saake G; Database and Software Engineering Group, Otto von Guericke University, Universitätsplatz 2, Magdeburg 39106, Germany., Heyer R; Multidimensional Omics Analyses Group, Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Bunsen-Kirchhoff-Straße 11, Dortmund 44139, Germany.; Faculty of Technology, Bielefeld University, Universitätsstraße 25, Bielefeld 33615, Germany.
Source:
Database : the journal of biological databases and curation [Database (Oxford)] 2023 Jul 10; Vol. 2023.
Publication Type:
Review; Journal Article; Research Support, Non-U.S. Gov't
Language:
English
Journal Info:
Publisher: Oxford Journals Country of Publication: England NLM ID: 101517697 Publication Model: Print Cited Medium: Internet ISSN: 1758-0463 (Electronic) Linking ISSN: 17580463 NLM ISO Abbreviation: Database (Oxford) Subsets: MEDLINE
Imprint Name(s):
Original Publication: Oxford : Oxford Journals, 2009-
MeSH Terms:
Algorithms* , Database Management Systems*, Databases, Factual ; Cluster Analysis
Comments:
Erratum in: Database (Oxford). 2024 Jan 20;2024:baae006. doi: 10.1093/database/baae006. (PMID: 38245003)
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Entry Date(s):
Date Created: 20230710 Date Completed: 20230712 Latest Revision: 20240120
Update Code:
20250114
PubMed Central ID:
PMC10332447
DOI:
10.1093/database/baad045
PMID:
37428679
Database:
MEDLINE

Weitere Informationen

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 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. Graphical abstract.
(© The Author(s) 2023. Published by Oxford University Press.)