pyBRAvo

pyBRAvo integrates BioPAX, the PathwayCommons knowledge graph, and SPARQL to reconstruct regulatory and signaling networks from linked biological data for network analysis.

Key Features:

• Semantic Web Integration: Uses the BioPAX ontology, the PathwayCommons knowledge graph, and SPARQL to query and semantically integrate distributed biological data sources.
• Automatic Network Reconstruction: Automatically reconstructs regulatory and signaling networks while addressing source-specific identification of biological entities and relationships.
• Redundancy Resolution: Resolves overlapping information across multiple life-science databases to reduce redundancy during network assembly.
• Logical Flow Recovery: Recovers logical flows within biological pathways to support accurate network modeling and analysis.

Scientific Applications:

• Tumor Cell Modeling: Applied to datasets such as 910 gene expression measurements from liver cancer patients to reconstruct regulatory and signaling networks for tumor cell modeling and drug screening.

Methodology:

pyBRAvo queries linked open databases using SPARQL against the PathwayCommons knowledge graph, leverages the BioPAX ontology to integrate retrieved data, and automates the assembly of reconstructed regulatory and signaling networks.