Datasets:

Y-Tarl
/

BioGraphFusion

---
license: apache-2.0
task_categories:
- graph-ml
- tabular-classification
language:
- en
tags:
- biology
- bioinformatics
- knowledge-graph
- graph-neural-networks
- drug-discovery
- medical
- disease-gene-prediction
- protein-chemical-interaction
- medical-ontology
size_categories:
- 100K<n<1M
---


# BioGraphFusion Dataset

[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![Paper](https://img.shields.io/badge/Paper-Bioinformatics-green.svg)](https://doi.org/10.1093/bioinformatics/btaf408)
[![arXiv](https://img.shields.io/badge/arXiv-2507.14468-b31b1b.svg)](https://arxiv.org/abs/2507.14468)

## 📊 Dataset Description

This dataset contains the benchmark data used in the paper **"BioGraphFusion: Graph Knowledge Embedding for Biological Completion and Reasoning"** published in *Bioinformatics*.

## 🏗️ Dataset Structure

The dataset includes three biomedical knowledge graph completion tasks with background knowledge integration:

### 1. Disease-Gene Prediction (DisGeNet_cv)



- **Task**: Disease-gene association prediction

- **Background Knowledge**: Drug-Disease relationships from SIDER (14,631 triples) + Protein-Chemical relationships from STITCH (277,745 triples)

- **Main Dataset**: DisGeNet (130,820 triples) focusing on gene targets

- **Description**: Predicts disease-gene associations using multi-source biological knowledge



### 2. Protein-Chemical Interaction (STITCH)



- **Task**: Protein-chemical interaction prediction

- **Background Knowledge**: Drug-Disease relationships from SIDER (14,631 triples) + Disease-Gene relationships from DisGeNet (130,820 triples)

- **Main Dataset**: STITCH (23,074 triples) focusing on chemical targets

- **Description**: Predicts protein-chemical interactions with integrated disease and gene knowledge



### 3. Medical Ontology Reasoning (UMLS)



- **Task**: Medical concept reasoning

- **Background Knowledge**: Various medical relationships from UMLS (4,006 triples)

- **Main Dataset**: UMLS (2,523 triples) with multi-domain entities

- **Description**: Reasons about medical concepts and their hierarchical relationships



## 📈 Dataset Statistics



| Dataset | Task | Background Knowledge Sources | Main Dataset Targets | Total Triples |

|---------|------|------------------------------|---------------------|---------------|

| **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 |

| **Medical Ontology Reasoning** | Medical concept reasoning | Various Medical Relationships UMLS (4,006) | UMLS (2,523) Multi-domain Entities | ~6.5K |



## 💻 Usage



### Loading the Dataset



```python

from datasets import load_dataset

# Load the complete dataset
dataset = load_dataset("Y-TARL/BioGraphFusion")



# Load specific task

disgenet_data = load_dataset("Y-TARL/BioGraphFusion", "Disease-Gene")

stitch_data = load_dataset("Y-TARL/BioGraphFusion", "Protein-Chemical") 

umls_data = load_dataset("Y-TARL/BioGraphFusion", "umls")

```



## 📝 Citation



If you use this dataset in your research, please cite our paper:



```bibtex

@article{lin2025biographfusion,

  title={BioGraphFusion: Graph Knowledge Embedding for Biological Completion and Reasoning},

  author={Lin, Yitong and He, Jiaying and Chen, Jiahe and Zhu, Xinnan and Zheng, Jianwei and Tao, Bo},

  journal={Bioinformatics},

  pages={btaf408},

  year={2025},

  publisher={Oxford University Press}

}

```



## 🔗 Related Resources



- **Paper**: [Bioinformatics](https://doi.org/10.1093/bioinformatics/btaf408)

- **Preprint**: [arXiv:2507.14468](https://arxiv.org/abs/2507.14468)

- **Code**: [GitHub Repository](https://github.com/Y-TARL/BioGraphFusion)



## 📄 License



This dataset is released under the Apache 2.0 License.



## 🙏 Acknowledgements



We thank the original data providers:



- DisGeNet for disease-gene associations

- STITCH for protein-chemical interactions

- UMLS for medical ontology data



## 📞 Contact



For questions about the dataset, please open an issue in the [GitHub repository](https://github.com/Y-TARL/BioGraphFusion/issues).