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Updated: Feb 24, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Informatics Support for Basic Research in Biomedicine.

Thomas C Rindflesch1, Catherine L Blake1, Marcelo Fiszman1

  • 1Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, Maryland. School of Information Sciences, University of Illinois, Urbana-Champaign; Center for Informatics in Science and Scholarship. Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, Maryland. Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, Maryland. Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, Maryland. School of Information Sciences, University of Illinois Urbana-Champaign, Champaign, Illinois. Yale University School of Medicine, New Haven, Connecticut.

ILAR Journal
|August 26, 2017
PubMed
Summary
This summary is machine-generated.

Biomedical informatics tools like Semantic MEDLINE aid researchers by mining text for entities and relations. Discovery browsing helps navigate complex research areas, exemplified by Alzheimer's disease mechanisms.

Keywords:
Alzheimer’s diseasebiomedical informaticsdiscovery browsingnatural language processingsortilintext mining

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Text Mining

Background:

  • Biomedical research generates vast amounts of data, necessitating efficient information management.
  • Text mining techniques can identify key entities (genes, diseases) and relationships within scientific literature.
  • Existing tools often lack integrated approaches for literature retrieval, concept identification, and visualization.

Purpose of the Study:

  • To present Semantic MEDLINE, an application for exploring biomedical research literature.
  • To introduce "discovery browsing," an informatics method for navigating complex research landscapes.
  • To demonstrate the utility of these tools in understanding Alzheimer’s disease research.

Main Methods:

  • Overview of text mining techniques for entity and relation extraction.
  • Description of Semantic MEDLINE's integrated PubMed retrieval, concept/relation identification, and visualization.
  • Application of "discovery browsing" for iterative learning and hypothesis generation.

Main Results:

  • Semantic MEDLINE facilitates pattern discernment in large citation sets.
  • "Discovery browsing" effectively narrows focus, as shown in Alzheimer's research.
  • Identified 73 citations on sortilin and Alzheimer's disease, with 15 detailed.
  • Found consensus on increased sortilin expression linked to Alzheimer's pathology.

Conclusions:

  • Semantic MEDLINE and discovery browsing offer powerful informatics solutions for biomedical research.
  • These tools support hypothesis formation and detailed exploration of specific research areas.
  • Research suggests a link between sortilin expression and Alzheimer's disease mechanisms.