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Graph4Med: a web application and a graph database for visualizing and analyzing medical databases.

Jero Schäfer1, Ming Tang2,3, Danny Luu2

  • 1Institute of Computer Science, Goethe-Universität Frankfurt, Frankfurt am Main, Germany. jeschaef@cs.uni-frankfurt.de.

BMC Bioinformatics
|December 12, 2022
PubMed
Summary
This summary is machine-generated.

Graph4Med transforms relational medical data into a graph database for enhanced patient cohort analysis. This enables faster, interactive exploration of complex data, including Next Generation Sequencing (NGS) results, for improved insights.

Keywords:
Data explorationGraph databaseMedical databaseVisualizationWeb application

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Area of Science:

  • Bioinformatics
  • Medical Informatics
  • Data Science

Background:

  • Medical databases, often relational, struggle with complex data exploration and visualization.
  • Emerging biomedical technologies like Next Generation Sequencing (NGS) generate vast, complex data, exceeding relational database capabilities.
  • Valuable insights into patient cohorts, distributions, and similarities are often missed due to data extraction challenges.

Purpose of the Study:

  • To present Graph4Med, a web application utilizing a graph database for efficient medical data analysis.
  • To enable straightforward visualization and analysis of patient cohorts, including complex genomic data.
  • To overcome limitations of relational databases in handling diverse and large-scale medical datasets.

Main Methods:

  • Transformed a relational database into a graph database using Neo4j.
  • Developed a suitable graph data schema for medical data, including routine records and NGS data.
  • Built a dashboard with NeoDash for querying and visualizing patient cohort information and similarity searches.

Main Results:

  • Graph4Med provides quick overviews of patient cohort distributions (gender, age, mutations, diagnosis).
  • Enables mutation (fusion)-based similarity searches and interactive patient graph generation.
  • Facilitates the identification of complex patterns and relationships within patient cohorts.

Conclusions:

  • Demonstrated the feasibility and advantages of graph databases for medical data storage and querying.
  • The dashboard offers fast, interactive analysis and visualization of complex medical data, particularly for NGS-derived mutation data.
  • Graph4Med can uncover hidden relationships in patient cohorts, offering novel diagnostic and research insights.