{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T12:44:50Z","timestamp":1780577090228,"version":"3.54.1"},"reference-count":204,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T00:00:00Z","timestamp":1688947200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"German Research Foundation","doi-asserted-by":"crossref","award":["HE 8077\/2-1 SA 465\/53-1"],"award-info":[{"award-number":["HE 8077\/2-1 SA 465\/53-1"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001659","name":"German Research Foundation","doi-asserted-by":"crossref","award":["HE 8077\/2-1 SA 465\/53-1"],"award-info":[{"award-number":["HE 8077\/2-1 SA 465\/53-1"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>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.<\/jats:p>\n               <jats:p>Graphical abstract \u00a0<\/jats:p>","DOI":"10.1093\/database\/baad045","type":"journal-article","created":{"date-parts":[[2023,7,11]],"date-time":"2023-07-11T00:06:36Z","timestamp":1689033996000},"source":"Crossref","is-referenced-by-count":14,"title":["The importance of\u00a0graph databases and\u00a0graph learning for\u00a0clinical applications"],"prefix":"10.1093","volume":"2024","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5094-3566","authenticated-orcid":false,"given":"Daniel","family":"Walke","sequence":"first","affiliation":[{"name":"Bioprocess Engineering, Otto von Guericke University , Universit\u00e4tsplatz 2, Magdeburg 39106, Germany"},{"name":"Database and Software Engineering Group, Otto von Guericke University , Universit\u00e4tsplatz 2, Magdeburg 39106, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Micheel","sequence":"additional","affiliation":[{"name":"Database and Software Engineering Group, Otto von Guericke University , Universit\u00e4tsplatz 2, Magdeburg 39106, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kay","family":"Schallert","sequence":"additional","affiliation":[{"name":"Multidimensional Omics Analyses Group, Leibniz-Institut f\u00fcr Analytische Wissenschaften\u2014ISAS\u2014e.V. , Bunsen-Kirchhoff-Stra\u00dfe 11, Dortmund 44139, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8304-2684","authenticated-orcid":false,"given":"Thilo","family":"Muth","sequence":"additional","affiliation":[{"name":"Section eScience (S.3), Federal Institute for Materials Research and Testing (BAM) , Unter den Eichen 87, Berlin 12205, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Broneske","sequence":"additional","affiliation":[{"name":"Infrastructure and Methods, German Center for Higher Education Research and Science Studies (DZHW) , Lange Laube 12, Hannover 30159, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gunter","family":"Saake","sequence":"additional","affiliation":[{"name":"Database and Software Engineering Group, Otto von Guericke University , Universit\u00e4tsplatz 2, Magdeburg 39106, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Heyer","sequence":"additional","affiliation":[{"name":"Multidimensional Omics Analyses Group, Leibniz-Institut f\u00fcr Analytische Wissenschaften\u2014ISAS\u2014e.V. , Bunsen-Kirchhoff-Stra\u00dfe 11, Dortmund 44139, Germany"},{"name":"Faculty of Technology, Bielefeld University , Universit\u00e4tsstra\u00dfe 25, Bielefeld 33615, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,7,10]]},"reference":[{"key":"2024071009460825100_R1","doi-asserted-by":"crossref","DOI":"10.1155\/2015\/370194","article-title":"Big data analytics in healthcare","author":"Belle","year":"2015","journal-title":"Biomed. 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