{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T10:51:43Z","timestamp":1753354303396,"version":"3.37.3"},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T00:00:00Z","timestamp":1670803200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T00:00:00Z","timestamp":1670803200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100003042","name":"Else Kr\u00f6ner-Fresenius-Stiftung","doi-asserted-by":"publisher","award":["Promotionsprogramm DigiStrucMed 2020_EKPK.20"],"award-info":[{"award-number":["Promotionsprogramm DigiStrucMed 2020_EKPK.20"]}],"id":[{"id":"10.13039\/501100003042","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Bildung und Forschung","doi-asserted-by":"publisher","award":["LeibnizKILabor (grant no. 01DD20003)"],"award-info":[{"award-number":["LeibnizKILabor (grant no. 01DD20003)"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Johann Wolfgang Goethe-Universit\u00e4t, Frankfurt am Main"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Medical databases normally contain large amounts of data in a variety of forms. Although they grant significant insights into diagnosis and treatment, implementing data exploration into current medical databases is challenging since these are often based on a relational schema and cannot be used to easily extract information for cohort analysis and visualization. As a consequence, valuable information regarding cohort distribution or patient similarity may be missed. With the rapid advancement of biomedical technologies, new forms of data from methods such as Next Generation Sequencing (NGS) or chromosome microarray (array CGH) are constantly being generated; hence it can be expected that the amount and complexity of medical data will rise and bring relational database systems to a limit.<\/jats:p><\/jats:sec><jats:sec><jats:title>Description<\/jats:title><jats:p>We present Graph4Med, a web application that relies on a graph database obtained by transforming a relational database. Graph4Med provides a straightforward visualization and analysis of a selected patient cohort. Our use case is a database of pediatric Acute Lymphoblastic Leukemia (ALL). Along routine patients\u2019 health records it also contains results of latest technologies such as NGS data. We developed a suitable graph data schema to convert the relational data into a graph data structure and store it in Neo4j. We used NeoDash to build a dashboard for querying and displaying patients\u2019 cohort analysis. This way our tool (1) quickly displays the overview of patients\u2019 cohort information such as distributions of gender, age, mutations (fusions), diagnosis; (2) provides mutation (fusion) based similarity search and display in a maneuverable graph; (3) generates an interactive graph of any selected patient and facilitates the identification of interesting patterns among patients.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>We demonstrate the feasibility and advantages of a graph database for storing and querying medical databases. Our dashboard allows a fast and interactive analysis and visualization of complex medical data. It is especially useful for patients similarity search based on mutations (fusions), of which vast amounts of data have been generated by NGS in recent years. It can discover relationships and patterns in patients cohorts that are normally hard to grasp. Expanding Graph4Med to more medical databases will bring novel insights into diagnostic and research.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12859-022-05092-0","type":"journal-article","created":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T04:12:33Z","timestamp":1670818353000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Graph4Med: a web application and a graph database for visualizing and analyzing medical databases"],"prefix":"10.1186","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7727-1181","authenticated-orcid":false,"given":"Jero","family":"Sch\u00e4fer","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danny","family":"Luu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anke Katharina","family":"Bergmann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lena","family":"Wiese","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,12]]},"reference":[{"issue":"7","key":"5092_CR1","doi-asserted-by":"publisher","first-page":"e17508","DOI":"10.2196\/17508","volume":"22","author":"L Ismail","year":"2020","unstructured":"Ismail L, Materwala H, Karduck AP, Adem A. Requirements of health data management systems for biomedical care and research: scoping review. J Med Internet Res. 2020;22(7):e17508.","journal-title":"J Med Internet Res"},{"key":"5092_CR2","unstructured":"Lawrence R. How RDBMS delays the healthcare data revolution. 2019. https:\/\/www.marklogic.com\/blog\/rdbms-delays-healthcare-data-revolution\/."},{"issue":"24","key":"5092_CR3","doi-asserted-by":"publisher","first-page":"3107","DOI":"10.1093\/bioinformatics\/btt549","volume":"29","author":"CT Have","year":"2013","unstructured":"Have CT, Jensen LJ. Are graph databases ready for bioinformatics? Bioinformatics. 2013;29(24):3107.","journal-title":"Bioinformatics"},{"key":"5092_CR4","doi-asserted-by":"publisher","DOI":"10.1515\/9783110441413","volume-title":"Advanced data management","author":"L Wiese","year":"2015","unstructured":"Wiese L. Advanced data management. 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