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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Mining disease fingerprints from within genetic pathways.

Ahmed Ragab Nabhan1, Indra Neil Sarkar

  • 1Center for Clinical & Translational Science, University of Vermont, Burlington, VT, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|January 11, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for analyzing genetic pathways in diseases. The approach uses probabilistic models to identify disease pathway

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Area of Science:

  • Systems Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Biological networks offer insights into system-level knowledge from micro-level associations.
  • Analyzing human disease genetic pathways can reveal underlying functional mechanisms of disorders.

Purpose of the Study:

  • To develop an approach for structural pattern analysis and classification of genetic pathways.
  • To identify characteristic components ('fingerprints') of functionally annotated pathways using a probabilistic model.

Main Methods:

  • Developed a probabilistic model to capture pathway 'fingerprints'.
  • Employed a probability estimation procedure to search for fingerprints and refine model parameters.
  • Evaluated the approach on 56 pathways from the Kyoto Encyclopedia of Genes and Genomes across seven disease categories.

Main Results:

  • Achieved an average classification accuracy of up to approximately 77%.
  • Demonstrated the effectiveness of identified 'fingerprints' in distinguishing between disease pathways.

Conclusions:

  • The developed 'fingerprints' show potential for classifying and discovering genetic pathways.
  • This approach can contribute to a deeper understanding of disease mechanisms at a functional level.