dwb2023/hetionet-edges

Dataset Card for Hetionet

Dataset Overview

This dataset represents an integrative biomedical knowledge graph, constructed from 29 public resources, encoding relationships between various biomedical entities. It is primarily designed for drug repurposing, treatment prediction, and network-based biomedical research.

• Original Data Source: The edge list is derived from the original Hetionet GitHub repository.
• Acknowledgment: Full credit goes to the original authors for their contributions.

Dataset Details

Dataset Description

Hetionet is a heterogeneous biomedical graph that integrates genes, compounds, diseases, pathways, biological processes, molecular functions, cellular components, pharmacologic classes, side effects, and symptoms into a structured network.

The Hetionet Edges Dataset specifically captures the relationships (edges) between biomedical entities.

• Number of Nodes (Entities): 47,031 (across 11 types)
• Number of Edges (Relationships): 2,250,197 (across 24 metaedge types)
• Data Sources: 29 public biomedical resources

Dataset Attribution

• Curators: Daniel Scott Himmelstein, Antoine Lizee, Christine Hessler, Leo Brueggeman, Sabrina L Chen, Dexter Hadley, Ari Green, Pouya Khankhanian, Sergio E Baranzini
• Language: English
• License: CC-BY-4.0

Dataset Sources

• Primary Repository: neo4j.het.io
• Publication: Systematic integration of biomedical knowledge prioritizes drugs for repurposing (eLife, 2017)
• Demo Application: het.io/repurpose

Intended Uses

Appropriate Use Cases

This dataset can be used for:

• Drug Repurposing Research: Identifying new uses for existing drugs.
• Treatment Prediction: Modeling biomedical relationships to predict treatment outcomes.
• Biomedical Knowledge Integration: Aggregating multiple datasets into a structured knowledge graph.
• Network Analysis of Biomedical Relationships: Exploring connectivity patterns between genes, diseases, and compounds.
• Computational Drug Efficacy Prediction: Using machine learning to assess potential drug efficacy.

Limitations and Out-of-Scope Use Cases

This dataset should not be used as:

• A stand-alone clinical decision-making tool without external validation.
• A replacement for experimental research or clinical trials.
• An authoritative guide for medical treatment recommendations.

Dataset Structure

Features

The dataset is formatted as a TSV file (tab-separated values) with three primary features (columns):

Feature	Description
source	The starting node of an edge, typically a gene, compound, or disease.
metaedge	The relationship type connecting the source and target node, defining how they interact.
target	The ending node of an edge, typically a gene, compound, disease, or biological process.

Metadata Summary

• Total unique source nodes: 20,138
• Total unique target nodes: 44,204
• Total unique metaedge types: 24
• Most common metaedge: GpBP (Gene participates in Biological Process) with 559,504 edges.

Metaedge Descriptions (Relationship Types)

Metaedge	Name	Student-Friendly Description
AdG	Anatomy disjoint with Anatomy	Identifies anatomical regions that are independent, meaning they cannot share the same condition or direct influence. This supports reasoning about anatomical independence.
AdSe	Anatomy disjoint with Side Effect	Highlights anatomical regions that cannot exhibit specific side effects, important for understanding regional drug safety implications.
CcG	Compound confers resistance to Gene	Describes how a chemical compound can mitigate or block the effects of a gene, especially relevant for drug resistance mechanisms.
CpD	Compound perturbs Disease	Indicates how a chemical compound alters the state or progression of a disease, providing insights into therapeutic mechanisms.
CpSE	Compound perturbs Side Effect	Explains how a compound can cause or influence side effects, essential for evaluating drug safety profiles.
DdG	Disease disjoint with Disease	Represents diseases that are mutually exclusive, meaning they cannot co-occur, aiding in differential diagnosis.
DdSe	Disease disjoint with Side Effect	Identifies side effects that cannot co-occur with specific diseases, supporting diagnostic accuracy.
DlG	Disease locally influences Gene	Describes the localized effects of a disease on gene activity in specific tissues or regions.
DlSe	Disease locally influences Side Effect	Indicates localized side effects caused by a disease, helping to map region-specific symptoms.
DpD	Disease perturbs Disease	Explains how one disease can exacerbate or alter the progression of another, highlighting disease-disease interactions.
DpG	Disease perturbs Gene	Describes how a disease disrupts or modifies the activity of a gene, crucial for understanding molecular pathogenesis.
DpSe	Disease perturbs Side Effect	Indicates side effects that arise as a consequence of a disease, supporting patient outcome predictions.
DrD	Disease reverses Disease	Represents cases where one disease counteracts or alleviates another, offering insights into possible therapeutic relationships.
DrG	Disease reverses Gene	Highlights how a disease can counteract or neutralize gene activity, which can inform treatment strategies.
DrSe	Disease reverses Side Effect	Indicates when a disease reduces or prevents specific side effects, aiding in therapeutic planning.
DuG	Disease upregulates Gene	Describes how a disease increases the activity of a gene, important for understanding its molecular effects.
DuSe	Disease upregulates Side Effect	Highlights how a disease amplifies specific side effects, helping to assess its broader impact.
GcG	Gene confers resistance to Gene	Represents genetic interactions where one gene protects against or mitigates the effects of another.
GdG	Gene disjoint with Gene	Identifies genes that cannot be active simultaneously, helping to elucidate gene regulation.
GdSe	Gene disjoint with Side Effect	Highlights genes that are not associated with specific side effects, useful for evaluating genetic contributions to drug reactions.
GpBP	Gene participates in Biological Process	Links genes to biological processes, foundational for understanding their role in cellular and organismal functions.
GpCc	Gene participates in Cellular Component	Maps where gene products are localized within cells, key for understanding their cellular roles.
GpD	Gene participates in Disease	Connects genes to diseases, enabling insights into genetic contributions to pathology.
GpG	Gene participates in Gene	Describes cooperative or functional relationships between genes, central to understanding genetic networks.
GpP	Gene participates in Pathway	Links genes to pathways, elucidating their role in complex biological processes.
GpS	Gene participates in Side Effect	Explains genetic contributions to side effects, essential for advancing personalized medicine.
GpT	Gene participates in Tissue	Maps genes to the tissues in which they are active, critical for tissue-specific research.
GpU	Gene participates in Pharmacologic Class	Associates genes with pharmacological classes of compounds, valuable for pharmacogenomics and drug discovery.
GpX	Gene participates in Symptom	Links genes to symptoms, helping to explain their genetic underpinnings and diagnostic relevance.
GrG	Gene reverses Gene	Represents interactions where one gene counteracts the effects of another, relevant for genetic therapy.
GrP	Gene reverses Pathway	Describes how genes can inhibit or deactivate specific biological pathways, important for therapeutic interventions.
GtG	Gene targets Gene	Represents direct regulatory or targeting relationships between genes, fundamental for understanding gene control mechanisms.
GuG	Gene upregulates Gene	Explains how one gene increases the activity of another, providing insights into regulatory networks.
GvG	Gene varies expression with Gene	Highlights genes with co-varying expression levels, aiding in the study of gene co-expression patterns.
GxG	Gene interacts with Gene	Represents general interactions between genes, foundational for systems biology and genetic research.

Dataset Creation

Curation Rationale

This dataset was created to improve drug repurposing research and computational drug efficacy prediction by leveraging heterogeneous biomedical relationships. The dataset integrates 755 known drug-disease treatments, supporting network-based reasoning for drug discovery.

• 🚨 Last Update: The edge list (hetionet-v1.0-edges.sif.gz) was last modified 7 years ago, and the node list (hetionet-v1.0-nodes.tsv) was last modified 9 years ago.
• Implications: While the dataset is a valuable resource, some biomedical relationships may be outdated, as new drugs, pathways, and gene-disease links continue to be discovered.

Source Data

Data Collection and Processing

• Data was aggregated from 29 public biomedical resources and integrated into a heterogeneous network.
• Community Feedback: The project incorporated real-time input from 40 community members to refine the dataset.
• Formats: The source dataset is available in TSV (tabular), JSON, and Neo4j formats.

Data Provenance & Last Update

Dataset File	Last Modified	Notes
hetionet-v1.0-nodes.tsv	9 years ago	Node table with biomedical entities
hetionet-v1.0-edges.sif.gz	7 years ago	Edge list with biomedical relationships
README.md	7 years ago	Recommends JSON/Neo4j formats for full metadata

Who are the source data producers?

• The dataset integrates biomedical knowledge from 29 public resources.
• Curated by: The University of California, San Francisco (UCSF) research team.
• Primary repository: Hetionet GitHub.

Bias, Risks, and Limitations

Key Limitations Due to Dataset Age

• 🚨 Data Recency: The dataset was last updated 7–9 years ago, meaning some relationships may be outdated due to advances in biomedical research.
• Network Incompleteness: Biomedical knowledge evolves, and newer discoveries are not reflected in this dataset.
• Bias in Source Data: Public biomedical databases have inherent biases based on what was known at the time of their last update.

Key Recommendations

✅ Users should verify relationships against more recent biomedical datasets.
✅ Use the JSON or Neo4j formats if metadata (license, attribution) is needed.
✅ Cross-check with external databases such as DrugBank, KEGG, or CTD.
✅ Consider integrating newer biomedical datasets for up-to-date analysis.

Citation

BibTeX:

  @article {10.7554/eLife.26726,
  article_type = {journal},
  title = {Systematic integration of biomedical knowledge prioritizes drugs for repurposing},
  author = {Himmelstein, Daniel Scott and Lizee, Antoine and Hessler, Christine and Brueggeman, Leo and Chen, Sabrina L and Hadley, Dexter and Green, Ari and Khankhanian, Pouya and Baranzini, Sergio E},
  editor = {Valencia, Alfonso},
  volume = 6,
  year = 2017,
  month = {sep},
  pub_date = {2017-09-22},
  pages = {e26726},
  citation = {eLife 2017;6:e26726},
  doi = {10.7554/eLife.26726},
  url = {https://doi.org/10.7554/eLife.26726},
  journal = {eLife},
  issn = {2050-084X},
  publisher = {eLife Sciences Publications, Ltd}
}

Additional Citations

• Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes

Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes
Himmelstein DS, Baranzini SE
PLOS Computational Biology (2015)
DOI: https://doi.org/10.1371/journal.pcbi.1004259 · PMID: 26158728 · PMCID: PMC4497619

Dataset Card Authors

dwb2023

Dataset Card Contact

dwb2023