SuppKG
Overview
SuppKG (Dietary Supplement Knowledge Graph) is a comprehensive knowledge graph that integrates information about dietary supplements and their relationships with diseases, genes, proteins, chemicals, and other biomedical entities. Developed as part of the NCATS Biomedical Data Translator program, SuppKG addresses the critical need for structured, computable knowledge about dietary supplements to support evidence-based clinical decision-making and translational research.
Key Features
- Comprehensive Coverage: Integrates diverse data about dietary supplements including ingredients, biological activities, and health effects
- Multi-Entity Relationships: Links supplements to diseases, genes, proteins, pathways, and chemical compounds
- Evidence-Based: Relationships supported by literature and clinical evidence
- Standardized Representation: Uses Biolink Model for semantic interoperability
- TRAPI-Compatible: Accessible through the Translator Reasoner API
- Integration with Translator: Part of the NCATS Translator knowledge provider ecosystem
Data Content
Entities
- Dietary Supplements: Vitamins, minerals, herbs, botanicals, amino acids, and other supplement products
- Supplement Ingredients: Active and inactive components
- Diseases and Conditions: Health conditions associated with supplement use
- Genes and Proteins: Molecular targets and mechanisms of action
- Chemical Compounds: Chemical structures and identifiers
- Biological Pathways: Mechanistic pathways affected by supplements
Relationships
- Supplement-disease associations (therapeutic uses, contraindications)
- Supplement-gene interactions
- Supplement-protein interactions
- Supplement-chemical compound relationships
- Ingredient-supplement compositions
- Mechanism of action relationships
- Drug-supplement interactions
Data Sources
SuppKG integrates information from multiple authoritative sources:
- Scientific literature (PubMed)
- Clinical databases
- Natural product databases
- Chemical databases (ChEBI, PubChem)
- Protein databases (UniProt)
- Pathway databases
- FDA and regulatory information
Applications
Clinical Decision Support
- Patient Safety: Identifying potential drug-supplement interactions
- Treatment Planning: Evidence-based supplement recommendations
- Contraindication Checking: Warning about unsafe supplement use in specific conditions
- Personalized Medicine: Tailoring supplement recommendations based on patient profiles
Research and Discovery
- Hypothesis Generation: Discovering novel supplement-disease associations
- Mechanism Exploration: Understanding biological mechanisms of supplement effects
- Repurposing Opportunities: Identifying new therapeutic applications for supplements
- Literature Mining: Systematic extraction of supplement knowledge from publications
Public Health
- Evidence Synthesis: Aggregating evidence about supplement efficacy and safety
- Policy Support: Informing regulatory decisions about dietary supplements
- Consumer Education: Providing accurate, evidence-based information about supplements
Technical Implementation
Knowledge Graph Structure
- Format: RDF-based knowledge graph
- Ontologies: Biolink Model for semantic standardization
- Identifiers: Standard biomedical identifiers (e.g., CUI, ChEBI, UniProt)
- Provenance: Comprehensive tracking of data sources and evidence
Access Methods
- Translator API: Query via TRAPI (Translator Reasoner API)
- Knowledge Provider: Accessible through Translator ARS (Autonomous Relay System)
- Programmatic Access: RESTful API endpoints
- SPARQL: Semantic queries for advanced users
Integration with Translator Ecosystem
SuppKG serves as a Knowledge Provider (KP) in the NCATS Translator program:
- Responds to queries about dietary supplements
- Integrates with other Translator KPs and ARAs
- Supports multi-hop reasoning across biomedical domains
- Contributes to comprehensive translational research queries
This resource has the Information Resource identifier: infores:suppkg
Publication
SuppKG is described in:
- Title: “SuppKG: A knowledge graph for dietary supplements”
- DOI: https://doi.org/10.1016/j.jbi.2022.104120
- Year: 2022
For more information, visit the SuppKG wiki or the NCATS Translator website.
Automated Evaluation