harmonizome-kg

is a Knowledge Graph.

Harmonizome-KG is a comprehensive knowledge graph derived from the Harmonizome database, integrating functional genomics data across multiple biological domains to connect genes with their functional annotations, regulatory relationships, protein interactions, and phenotypic associations in a unified graph structure.

Domains

biomedical, genomics, systems biology, biological systems

License

CC BY 4.0

Homepage

harmonizome-kg

Repository

GitHub

Infores ID

Unknown

FAIRsharing ID

Unknown

Product Summary

Products

From this Resource
ID Name URL Category Format Description
harmonizome-kg.portal Harmonizome-KG Explorer harmonizome-kg GraphicalInterface http Interactive web interface for explori...
harmonizome-kg.api Harmonizome-KG API api ProgrammingInterface http RESTful API for programmatic access t...
harmonizome-kg.graph Harmonizome-KG Neo4j Database GraphProduct neo4j Neo4j database containing integrated ...

Details

Harmonizome-KG

Harmonizome-KG is a comprehensive knowledge graph built from the Harmonizome database, which contains over 100 datasets from 66 online resources that describe functional associations between genes and proteins. The knowledge graph provides a unified view of functional genomics data across multiple biological scales and domains.

Key Features

Comprehensive Data Integration

  • Integrates over 100 curated functional genomics datasets
  • Spans protein-protein interactions, gene expression, genetic associations, and regulatory relationships
  • Contains data from 66 diverse online biological resources
  • Covers multiple species with primary focus on human and mouse

Multi-Scale Biological Networks

  • Gene-protein interaction networks from high-throughput screens
  • Transcriptional regulatory networks from ChIP-seq and motif analysis
  • Metabolic pathway associations from curated databases
  • Disease-gene associations from clinical and genetic studies
  • Drug-target relationships from pharmacological databases

Harmonized Data Structure

  • Standardized gene and protein identifiers across datasets
  • Normalized scoring systems for relationship strengths
  • Consistent metadata annotation for all data sources
  • Cross-reference mappings between different identifier systems

Data Categories

Protein Interactions

  • Physical protein-protein interactions from mass spectrometry
  • Functional interactions from genetic screens
  • Protein complexes from structural and biochemical studies
  • Domain-domain interaction predictions

Gene Expression

  • Tissue-specific expression patterns from RNA-seq
  • Single-cell expression profiles across cell types
  • Condition-specific expression changes
  • Developmental stage expression dynamics

Regulatory Networks

  • Transcription factor binding sites from ChIP-seq
  • MicroRNA target predictions and validations
  • Epigenetic modifications and chromatin states
  • Enhancer-promoter interaction maps

Phenotypic Associations

  • Gene-disease associations from clinical studies
  • Drug response and pharmacogenomic data
  • Model organism phenotype annotations
  • Genetic variant effect predictions

Applications

Network Medicine

  • Disease module identification and characterization
  • Drug target prioritization and mechanism analysis
  • Biomarker discovery through network propagation
  • Pathway-based drug repurposing strategies

Functional Annotation

  • Gene function prediction through network analysis
  • Protein complex inference and validation
  • Regulatory pathway reconstruction
  • Cross-species functional conservation

Systems Analysis

  • Multi-omics data integration and analysis
  • Network-based feature selection for machine learning
  • Identification of key regulatory hubs and bottlenecks
  • Evolutionary analysis of functional networks

Technical Implementation

The Harmonizome-KG is implemented as a Neo4j graph database with standardized node types for genes, proteins, pathways, diseases, drugs, and other biological entities. Relationships are weighted based on confidence scores derived from experimental evidence and computational predictions.

Automated Evaluation

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Created: September 23, 2025 | Last modified: September 23, 2025