pyBiodatafuse

pyBiodatafuse integrates biomedical databases through a modular, query-based framework to generate context-specific knowledge graphs from gene or metabolite identifiers.

Key Features:

• Modular data integration: Establishes a modular framework that simplifies data wrangling across diverse biomedical databases.
• Dynamic knowledge graph generation: Creates context-specific knowledge graphs on the fly from a set of gene or metabolite identifiers.
• Graph-based analysis support: Enables graph-based analyses on the generated knowledge graphs.
• Platform plugins: Offers plugins for Cytoscape, Neo4j, and GraphDB for local hosting of property and RDF graphs.

Scientific Applications:

• Post-COVID syndrome research: Constructs knowledge graphs from differential gene expression data to build adaptable and context-specific knowledge representations for post-COVID syndrome.
• Omics data integration: Integrates omics data analysis with publicly available databases to unravel complex biological mechanisms.

Methodology:

pyBiodatafuse uses a query-based approach with a pipeline for generating context-specific knowledge graphs dynamically from gene or metabolite identifiers.