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Context-driven automatic subgraph creation for literature-based discovery.

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  • 1Ohio Center of Excellence in Knowledge-Enabled Computing (Kno.e.sis), Wright State University, Dayton, OH 45435, USA.

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Summary
This summary is machine-generated.

This study introduces an automated method for literature-based discovery (LBD) that uncovers hidden biomedical associations. The approach successfully rediscovered existing scientific findings, offering new insights into complex relationships between concepts.

Keywords:
Graph miningHierarchical agglomerative clusteringLiterature-based discovery (LBD)Medical Subject Headings (MeSH)Path clusteringSemantic relatedness

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Area of Science:

  • Biomedical informatics
  • Computational biology
  • Knowledge discovery

Background:

  • Literature-based discovery (LBD) aims to find hidden associations in scientific literature.
  • Existing LBD methods rely on manual filtering or purely statistical approaches, which have limitations in scalability and insight generation.
  • Graph-based approaches show potential but require significant a priori knowledge and manual effort.

Purpose of the Study:

  • To implement and evaluate a context-driven, automatic subgraph creation method for facilitating LBD.
  • To capture multifaceted complex associations between biomedical concepts.
  • To automatically generate ranked lists of informative subgraphs for potentially unknown associations.

Main Methods:

  • Collected MEDLINE articles containing specified concepts (A, C).
  • Extracted semantic predications to build a labeled directed graph.
  • Applied Hierarchical Agglomerative Clustering (HAC) to cluster concept-related paths based on MeSH semantics.
  • Generated ranked subgraphs based on semantic relatedness and shared context.

Main Results:

  • Successfully rediscovered 8 out of 9 existing scientific discoveries.
  • Identified intermediate concepts and provided insights into the meaning of associations.
  • Demonstrated potential for understanding complex associations across thematic dimensions (e.g., Cellular Activity, Pharmaceutical Treatment).
  • Statistically evaluated subgraphs, finding arbitrary associations mentioned in ~4 MEDLINE articles on average.

Conclusions:

  • Leveraging MeSH descriptors (implicit and explicit semantics) is effective for capturing context in LBD.
  • The automated subgraph creation method enhances understanding of complex associations across multiple thematic dimensions.
  • This approach facilitates more scalable and insightful literature-based discovery.