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| 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 |
1from datasets import load_dataset
2
3# Load the complete dataset
4dataset = load_dataset("Y-TARL/BioGraphFusion")
5
6# Load specific task
7disgenet_data = load_dataset("Y-TARL/BioGraphFusion", "Disease-Gene")
8stitch_data = load_dataset("Y-TARL/BioGraphFusion", "Protein-Chemical")
9umls_data = load_dataset("Y-TARL/BioGraphFusion", "umls")1@article{lin2025biographfusion,
2 title={BioGraphFusion: Graph Knowledge Embedding for Biological Completion and Reasoning},
3 author={Lin, Yitong and He, Jiaying and Chen, Jiahe and Zhu, Xinnan and Zheng, Jianwei and Tao, Bo},
4 journal={Bioinformatics},
5 pages={btaf408},
6 year={2025},
7 publisher={Oxford University Press}
8}