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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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SemNet: Using Local Features to Navigate the Biomedical Concept Graph.

Andrew R Sedler1, Cassie S Mitchell1

  • 1Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology, Emory University School of Medicine, Atlanta, GA, United States.

Frontiers in Bioengineering and Biotechnology
|July 24, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces SemNet, an open-source Python software for literature-based discovery (LBD). SemNet builds a biomedical concept graph from PubMed abstracts, enabling novel hypothesis generation for research and clinical care.

Keywords:
Pythonliterature based discoverynatural language processingtext miningunsupervised learning

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Knowledge Discovery

Background:

  • Literature-based discovery (LBD) systems struggle with user adoption.
  • Existing LBD approaches often lack scalability and comprehensive integration of biomedical knowledge.

Purpose of the Study:

  • To develop an open-source Python software, SemNet, for large-scale literature-based discovery.
  • To create a semantic inference network and biomedical concept graph from PubMed abstracts.
  • To enable efficient identification of hidden connections and hypothesis generation.

Main Methods:

  • Utilized PubMed's 27.9 million abstracts to build a Neo4j graph database.
  • Represented unique United Medical Language System (UMLS) concepts as nodes and predications as edges.
  • Computed metapath-based features (count, degree weighted path count, HeteSim) and vectorized them.
  • Employed unsupervised learning for rank aggregation (ULARA) to rank relevant source nodes.
  • Analyzed correlations and high residual nodes for pattern identification and hypothesis generation.

Main Results:

  • Successfully converted semantic predications into a scalable semantic inference network and concept graph.
  • Demonstrated SemNet's capability to identify relevant source nodes for user-specified target nodes.
  • Generated a novel hypothesis regarding the differential impact of smoking on cognition in males and females.

Conclusions:

  • SemNet offers an adoptable and efficient method for literature-based discovery from PubMed.
  • The software facilitates multi-scalar connections beyond omics, providing actionable insights.
  • SemNet supports predictive medicine, research prioritization, and clinical care by uncovering hidden biomedical relationships.