dwb2023/hetionet-nodes

Dataset Card for Hetionet Nodes

Dataset Overview

This dataset comprises the node information from Hetionet, an integrative biomedical knowledge graph designed to facilitate drug repurposing research and treatment prediction. It encodes data from millions of biomedical studies, connecting various entities such as genes, diseases, and compounds. Full credit and appreciation are directed to the original authors for their significant contributions.

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

Dataset Details

Dataset Description

The Hetionet Nodes Dataset contains detailed information on 47,031 nodes spanning 11 distinct types, including:

• Genes
• Diseases
• Compounds
• Anatomies
• Pathways
• Biological Processes
• Molecular Functions
• Cellular Components
• Pharmacologic Classes
• Side Effects
• Symptoms

Each node is characterized by a unique identifier, a name, and a category (kind), facilitating comprehensive network analyses.

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

Dataset Sources

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

Uses

Direct Use

This dataset is designed 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.

Out-of-Scope Use

The dataset should not be used as:

• A definitive source for clinical decision-making without proper validation.
• A replacement for clinical trials or experimental validation.
• The sole basis for drug administration or medical treatment.

Dataset Structure

The dataset comprises 47,031 nodes categorized into 11 types. Each entry includes:

• id: A unique identifier formatted as Category::Namespace:ID (e.g., Gene::HGNC:5).
• name: The standard name of the entity (e.g., TP53).
• kind: The category of the entity (e.g., Gene).

Distribution of Node Types with Examples:

Kind	Count	Example Name
Gene	20,945	A1BG
Biological Process	11,381	Mitochondrial Genome Maintenance
Side Effect	5,734	Acute Abdomen
Molecular Function	2,884	Trans-Hexaprenyltranstransferase Activity
Pathway	1,822	α4β7 Integrin Signaling
Compound	1,552	Goserelin
Cellular Component	1,391	Phosphopyruvate Hydratase Complex
Symptom	438	Abdomen, Acute
Anatomy	402	Uterine Cervix
Pharmacologic Class	345	Calcium Channel Antagonists
Disease	137	Idiopathic Pulmonary Fibrosis

Examples:

• Gene: id: Gene::HGNC:5, name: A1BG, kind: Gene
• Disease: id: Disease::DOID:8398, name: Alzheimer's disease, kind: Disease

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 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 dataset is available in TSV (tabular), JSON, and Neo4j formats.
  • Hetionet’s README suggests JSON or Neo4j formats for full metadata (license, attribution, etc.).

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

Potential Biases

• 🚨 Data Recency: The dataset was last updated 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.

Recommendations for Users

To maximize reliability and scientific validity, users should:

• Validate computational predictions through experimental and clinical research.
• Check the publication dates of source data—scientific knowledge evolves, and outdated relationships may no longer be valid.
• Cross-reference findings with additional biomedical databases and scientific literature.
• Use this dataset in combination with laboratory evidence before making any biomedical conclusions.

Citation

When using this dataset, please cite the following publication:

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