| ---
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| license: apache-2.0
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| task_categories:
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| - graph-ml
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| - tabular-classification
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| language:
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| - en
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| tags:
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| - biology
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| - bioinformatics
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| - knowledge-graph
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| - graph-neural-networks
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| - drug-discovery
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| - medical
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| - disease-gene-prediction
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| - protein-chemical-interaction
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| - medical-ontology
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| size_categories:
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| - 100K<n<1M
|
| ---
|
|
|
| # BioGraphFusion Dataset
|
|
|
| [](https://opensource.org/licenses/Apache-2.0)
|
| [](https://doi.org/10.1093/bioinformatics/btaf408)
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| [](https://arxiv.org/abs/2507.14468)
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|
|
| ## ? Dataset Description
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|
|
| This dataset contains the benchmark data used in the paper **"BioGraphFusion: Graph Knowledge Embedding for Biological Completion and Reasoning"** published in *Bioinformatics*.
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|
|
| ## ?? Dataset Structure
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|
|
| The dataset includes three biomedical knowledge graph completion tasks with background knowledge integration:
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|
|
| ### 1. Disease-Gene Prediction (DisGeNet_cv)
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|
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| - **Task**: Disease-gene association prediction
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| - **Background Knowledge**: Drug-Disease relationships from SIDER (14,631 triples) + Protein-Chemical relationships from STITCH (277,745 triples)
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| - **Main Dataset**: DisGeNet (130,820 triples) focusing on gene targets
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| - **Description**: Predicts disease-gene associations using multi-source biological knowledge
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|
|
| ### 2. Protein-Chemical Interaction (STITCH)
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|
|
| - **Task**: Protein-chemical interaction prediction
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| - **Background Knowledge**: Drug-Disease relationships from SIDER (14,631 triples) + Disease-Gene relationships from DisGeNet (130,820 triples)
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| - **Main Dataset**: STITCH (23,074 triples) focusing on chemical targets
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| - **Description**: Predicts protein-chemical interactions with integrated disease and gene knowledge
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|
|
| ### 3. Medical Ontology Reasoning (UMLS)
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|
|
| - **Task**: Medical concept reasoning
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| - **Background Knowledge**: Various medical relationships from UMLS (4,006 triples)
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| - **Main Dataset**: UMLS (2,523 triples) with multi-domain entities
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| - **Description**: Reasons about medical concepts and their hierarchical relationships
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|
|
| ## ? Dataset Statistics
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|
|
| | Dataset | Task | Background Knowledge Sources | Main Dataset Targets | Total Triples |
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| |---------|------|------------------------------|---------------------|---------------|
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| | **Disease-Gene Prediction** | Disease-gene association prediction | Drug-Disease Relationships SIDER (14,631) + Protein-Chemical Relationships STITCH (277,745) | DisGeNet (130,820) Gene | ~423K |
|
| | **Protein-Chemical Interaction** | Protein-chemical interaction prediction | Drug-Disease Relationships SIDER (14,631) + Disease-Gene Relationships DisGeNet (130,820) | STITCH (23,074) Chemical | ~168K |
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| | **Medical Ontology Reasoning** | Medical concept reasoning | Various Medical Relationships UMLS (4,006) | UMLS (2,523) Multi-domain Entities | ~6.5K |
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|
|
| ## ? Usage
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|
|
| ### Loading the Dataset
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|
|
| ```python
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| from datasets import load_dataset
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|
|
| # Load the complete dataset
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| dataset = load_dataset("Y-TARL/BioGraphFusion")
|
|
|
| # Load specific task
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| disgenet_data = load_dataset("Y-TARL/BioGraphFusion", "Disease-Gene")
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| stitch_data = load_dataset("Y-TARL/BioGraphFusion", "Protein-Chemical")
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| umls_data = load_dataset("Y-TARL/BioGraphFusion", "umls")
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| ```
|
|
|
| ## ? Citation
|
|
|
| If you use this dataset in your research, please cite our paper:
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|
|
| ```bibtex
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| @article{lin2025biographfusion,
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| title={BioGraphFusion: Graph Knowledge Embedding for Biological Completion and Reasoning},
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| author={Lin, Yitong and He, Jiaying and Chen, Jiahe and Zhu, Xinnan and Zheng, Jianwei and Tao, Bo},
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| journal={Bioinformatics},
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| pages={btaf408},
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| year={2025},
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| publisher={Oxford University Press}
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| }
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| ```
|
|
|
| ## ? Related Resources
|
|
|
| - **Paper**: [Bioinformatics](https://doi.org/10.1093/bioinformatics/btaf408)
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| - **Preprint**: [arXiv:2507.14468](https://arxiv.org/abs/2507.14468)
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| - **Code**: [GitHub Repository](https://github.com/Y-TARL/BioGraphFusion)
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|
|
| ## ? License
|
|
|
| This dataset is released under the Apache 2.0 License.
|
|
|
| ## ? Acknowledgements
|
|
|
| We thank the original data providers:
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|
|
| - DisGeNet for disease-gene associations
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| - STITCH for protein-chemical interactions
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| - UMLS for medical ontology data
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|
|
| ## ? Contact
|
|
|
| For questions about the dataset, please open an issue in the [GitHub repository](https://github.com/Y-TARL/BioGraphFusion/issues).
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| |
