Table of Contents

• Democratising Knowledge Graphs
  • Mission Statement
  • Vision Statement
  • Connect your Knowledge Graph to Large Language Models
• User Experiences
  • A Knowledge Graph for Impact of Genomic Variation on Function (IGVF)
  • Drug Repurposing with CROssBAR
  • Builing a Knowledge Graph for Contextualised Metabolic-Enzymatic Interactions
• Get Involved!
• Installation
  • Install as a dependency for your KG pipeline
  • For developers
• Configuration
  • BioCypher settings
• Quickstart
  • The input adapter
  • The schema configuration YAML file
  • BioCypher API documentation
  • The biocypher configuration YAML file
• Adapters
  • BioCypher meta-graph
• Output
  • Neo4j
  • ArangoDB
  • CSV
  • RDF
  • PostgreSQL
  • SQLite
  • NetworkX
• Example Notebook: BioCypher and Pandas
  • Introduction
  • Setup
  • Configuration
  • Section 1: Adding data
  • Section 2: Merging data
  • Section 3: Handling properties
  • Section 4: Handling relationships
• Tutorial - Basics
  • Setup
  • Configuration
  • Section 1: Adding data
  • Section 2: Merging data
  • Section 3: Handling properties
  • Section 4: Handling relationships
• Tutorial - Handling Ontologies
  • What is an ontology?
  • How BioCypher uses ontologies
  • Visualising ontologies
  • Using ontologies: plain Biolink
  • Model extensions
  • Hybridising ontologies
• Tutorial - Adapters
  • Adapter philosophy
  • Adapter functions
  • 1. Loading the Data
  • 2. Passing the Data
  • Note: Strict mode
• Standalone Docker Image
• R and Bioconductor
• API Reference
  • The main BioCypher interface
  • Database creation by file
  • In-memory Pandas knowledge graph
  • Database creation and manipulation by Driver
  • Download and cache functionality
  • Ontology ingestion, parsing, and manipulation
  • Mapping of data inputs to KG ontology
  • Base classes for node and edge representations in BioCypher
  • Translation functionality for implemented types of representation
  • Logging
  • Miscellaneous utility functions