Add BioGraphFusion dataset

README.md

@@ -1,3 +1,95 @@
----
-license: apache-2.0
----
+# 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).