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Related Experiment Video

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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Discovering relations between indirectly connected biomedical concepts.

Dirk Weissenborn1, Michael Schroeder2, George Tsatsaronis2

  • 1DFKI Projektbüro Berlin, Alt-Moabit 91c, Berlin, 10559 Germany ; Biotechnology Center, Technische Universität Dresden, Tatzberg 47/49, Dresden, 01307 Germany.

Journal of Biomedical Semantics
|July 8, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for discovering hidden biomedical relations by analyzing indirect knowledge within a graph. This approach significantly improves the prediction of relationships like "has target" and "may treat".

Keywords:
Biomedical conceptsRelation discoveryText mining

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Area of Science:

  • Biomedical Informatics
  • Knowledge Representation and Reasoning
  • Machine Learning

Background:

  • The vast and complex biomedical knowledge landscape necessitates methods for integrating heterogeneous data from structured and unstructured sources.
  • Discovering hidden relationships between biomedical concepts is crucial for hypothesis generation and drawing informed conclusions.
  • Existing approaches face challenges in effectively combining diverse data types for comprehensive knowledge mining.

Purpose of the Study:

  • To develop a method for discovering hidden biomedical relations by leveraging indirect knowledge within a graph-based representation.
  • To integrate information from both structured (ontologies) and unstructured (textual) data sources into a unified knowledge graph.
  • To identify characteristic path patterns that signify specific biomedical relations.

Main Methods:

  • Constructed a biomedical knowledge graph where concepts are vertices and relations are edges, derived from ontologies and text.
  • Employed distant supervision to mine path patterns (sequences of relations) within the knowledge graph.
  • Utilized machine learning models to identify and learn expressive path patterns indicative of specific biomedical relations.

Main Results:

  • Successfully identified characteristic path patterns for biomedical relations, specifically 'has target' and 'may treat'.
  • Achieved an Area Under the Curve (AUC) up to 0.8 for relation discovery using indirect knowledge, a substantial improvement over random classification.
  • Demonstrated the feasibility of prioritizing accurate predictions by following the proposed indirect knowledge discovery approach.

Conclusions:

  • The developed models effectively learn expressive path patterns, enabling the discovery of indirect connections between biomedical concepts.
  • The constructed knowledge graph facilitates the seamless integration of heterogeneous biomedical information.
  • This approach offers a promising pathway for uncovering novel and previously unrecognized biomedical relationships.