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Related Concept Videos

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Semantically linking in silico cancer models.

David Johnson1, Anthony J Connor2, Steve McKeever3

  • 1Department of Computing, Imperial College London, London, UK. ; Data Science Institute, Imperial College London, London, UK.

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|December 19, 2014
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Summary

This study introduces a graph-based model to link computational cancer models across different biological scales. This approach enhances understanding and exploration of combined cancer models for better research discovery.

Keywords:
in silico oncologymodel explorationneo4jproperty graphstumor modeling

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Area of Science:

  • Computational biology
  • Cancer research
  • Systems biology

Background:

  • Multiscale models are crucial in cancer research but face challenges in integrating models developed independently.
  • Disparate methodologies, programming languages, and parameters hinder the understanding of inter-model interfaces and interactions.
  • A standardized approach is needed to link and explore combinations of computational cancer models effectively.

Purpose of the Study:

  • To introduce a graph-based model for semantically linking computational cancer models across various biological scales.
  • To facilitate a better understanding and exploration of combined cancer models.
  • To enhance interoperability and discoverability of cancer modeling resources.

Main Methods:

  • A graph-based model is proposed to represent and link computational cancer models.
  • The TumorML (an XML-based markup language) data model is transposed into a graph representation.
  • Domain models, including controlled vocabularies and ontologies, are linked with cancer model descriptions.
  • A connected property graph is created by uniting these domain and model graphs.

Main Results:

  • The graph-based approach enables linking cancer models through categorizations, computational compatibility, and semantic interoperability.
  • This creates a unified framework for exploring relationships between diverse cancer models.
  • Opportunities for discovering novel combinations of multiscale cancer models are identified.

Conclusions:

  • The proposed graph model provides a robust framework for integrating and understanding multiscale cancer models.
  • Semantic linking through domain graphs enhances the interoperability and accessibility of cancer modeling resources.
  • This facilitates advanced exploration and discovery in computational cancer research.