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Health Information Technology and Healthcare Information System01:30

Health Information Technology and Healthcare Information System

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Concepts of Health and Illness

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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

A Semantic Web management model for integrative biomedical informatics.

Helena F Deus1, Romesh Stanislaus, Diogo F Veiga

  • 1Department of Bioinformatics and Computational Biology, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, United States of America.

Plos One
|August 14, 2008
PubMed
Summary
This summary is machine-generated.

Semantic Web technologies enable integrated biomedical data management across multiple institutions. This facilitates distributed data sharing and analysis for systems biology and translational research.

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Area of Science:

  • Biomedical Informatics
  • Data Science
  • Computer Science

Background:

  • Life sciences generate vast, diverse data, posing challenges for integration and access control.
  • Integrating multi-domain, clinical, and molecular data is particularly complex.
  • Existing systems struggle with automated articulation and permission management.

Purpose of the Study:

  • To present a core model for biomedical knowledge engineering using Semantic Web technologies.
  • To demonstrate a distributed data management infrastructure for complex biomedical data.
  • To showcase interoperability between diverse data sources and analysis resources.

Main Methods:

  • Developed a core model for biomedical knowledge engineering applications.
  • Utilized Semantic Web technologies for data integration and management.
  • Created a software prototype (open source at www.s3db.org) for distributed data management.

Main Results:

  • Demonstrated a management model for hosting and managing intertwined data structures by multiple authorities.
  • Successfully linked data sources from Lung Cancer SPORE projects at two institutions.
  • Developed an open-source software prototype for shared, distributed data management.

Conclusions:

  • Semantic Web technologies offer solutions for distributed, evolvable data representations crucial for systems biology and translational research.
  • Future applications will increasingly integrate personal computing software with remote biomedical resources.
  • Automated data acquisition should trigger delegated analysis, enhancing research efficiency.