TY - JOUR A1 - Schäfer, Jero A1 - Tang, Ming A1 - Luu, Danny A1 - Bergmann, Anke Katharina A1 - Wiese, Lena T1 - Graph4Med: a web application and a graph database for visualizing and analyzing medical databases T2 - BMC Bioinformatics N2 - Background: 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. Description: 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’ 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’ cohort analysis. This way our tool (1) quickly displays the overview of patients’ 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. Conclusion: 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. KW - Data exploration KW - Graph database KW - Medical database KW - Visualization KW - Web application Y1 - 2022 UR - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/86363 UR - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-863630 SN - 1471-2105 N1 - Open Access funding enabled and organized by Projekt DEAL. N1 - Funding: Else Kröner-Fresenius-Stiftung ; DigiStrucMed 2020_EKPK.20 N1 - Funding: BMBF ; 01DD20003 N1 - Gefördert durch den Open-Access-Publikationsfonds der Goethe-Universität. N1 - The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. N1 - The clinical data used in our approach are not shared due to the preservation of the patients’ privacy. A public demonstrator with artificial data can be found on the Graph4Med website. Source code is located in the project repository. VL - 23.2022 IS - art. 537 SP - 1 EP - 22 PB - BioMed Central , Springer CY - London ; Berlin ; Heidelberg ER -