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Large-scale regulatory and signaling network assembly through linked open data.

M Lefebvre1, A Gaignard2, M Folschette3

  • 1UMR 1332 Biologie du Fruit et Pathologie, INRAE, Univ. Bordeaux, 72 Avenue Edouard Bourlaux, CS20032, 33882, Villenave d'Ornon cedex, France.

Database : the Journal of Biological Databases and Curation
|January 18, 2021
PubMed
Summary

We developed pyBRAvo, a Semantic Web tool to automate biological network reconstruction. This approach simplifies analyzing complex biological pathways for cancer research and drug discovery.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Organizing biological knowledge in linked open databases is crucial for network reconstruction.
  • Manual assembly of regulatory and signaling networks is challenging due to data redundancy and complexity.

Purpose of the Study:

  • To propose a Semantic Web-based framework for automating the reconstruction of large-scale biological networks.
  • To apply this framework to model tumor cell networks and aid drug screening.

Main Methods:

  • Developed pyBRAvo (python Biological netwoRk Assembly), a tool utilizing Semantic Web technologies.
  • Applied pyBRAvo to a dataset of 910 gene expression measurements from liver cancer patients.

Main Results:

  • Successfully automated the reconstruction of biological networks.
  • Demonstrated the tool's applicability in the context of liver cancer patient data.

Conclusions:

  • The pyBRAvo framework offers an automated solution for reconstructing complex biological networks.
  • This facilitates tumor cell modeling and drug screening, advancing cancer research.