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Related Experiment Videos

OWL-NETS: Transforming OWL Representations for Improved Network Inference.

Tiffany J Callahan1, William A Baumgartner, Michael Bada

  • 1Computational Bioscience Program, University of Colorado Denver Anschutz Medical Campus, Aurora, CO 80045, USA, tiffany.callahan@ucdenver.edu.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|December 9, 2017
PubMed
Summary
This summary is machine-generated.

OWL-NETS transforms biomedical knowledge into networks for machine learning, generating novel hypotheses to complete incomplete disease mechanism data. This method enhances knowledge bases with inferred biological insights.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Knowledge Representation

Background:

  • Complex human disease mechanisms remain incompletely understood.
  • Existing Semantic Web technologies (e.g., Web Ontology Language) struggle with missing or uncertain data.
  • Inductive inference methods like machine learning are crucial for knowledge discovery.

Purpose of the Study:

  • To develop a computational method for inductive inference on OWL-encoded biomedical knowledge.
  • To address limitations of current OWL reasoners in handling incomplete information.
  • To generate novel, biologically relevant hypotheses from existing knowledge bases.

Main Methods:

  • Proposed OWL-NETS (NEtwork Transformation for Statistical learning) method.
  • Reversible abstraction of OWL-encoded knowledge into a network representation.
  • Application of network inference methods on the transformed network.

Main Results:

  • OWL-NETS successfully generates novel, biologically-relevant hypotheses.
  • Demonstrated utility using examples from Open Biomedical Ontologies.
  • Lossless transformation enables seamless integration of inferred knowledge back into original bases.

Conclusions:

  • OWL-NETS facilitates inductive inference on rich OWL knowledge.
  • The method extends the coverage and completeness of biomedical knowledge bases.
  • Enables generation of new biological insights by combining ontology and network inference.