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---
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annotations_creators:
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- expert-generated
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language:
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- en
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license: mit
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pretty_name: BioMedGraphica
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tags:
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- biomedical
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- knowledge-graph
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- multi-omics
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- data-integration
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- graph-ml
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- drug-discovery
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- text-mining
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- bioinformatics
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size_categories:
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- 1M<n<5G
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task_categories:
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- graph-ml
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- token-classification
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- feature-extraction
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- other
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---
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# BioMedGraphica
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**BioMedGraphica** is an all-in-one platform for biomedical data integration and knowledge graph generation. It harmonizes fragmented biomedical datasets into a unified, graph AI-ready resource that facilitates precision medicine, therapeutic target discovery, and integrative biomedical AI research.
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Developed using data from **43 biomedical databases**, BioMedGraphica integrates:
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- **11 entity types**
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- **30 relation types**
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- Over **2.3 million entities** and **27 million relations**
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## ✨ Highlights
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- **Multi-omics integration**: Genomic, transcriptomic, proteomic, metabolomic, microbiomic, exposomic
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- **Graph AI-ready**: Outputs subgraphs ready for GNNs and ML models
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- **Soft matching**: Uses BioBERT for fuzzy entity resolution (disease, phenotype, drug, exposure)
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- **GUI software**: Provides Windows-based interface for end-to-end pipeline
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- **Connected graph variant**: Isolated nodes removed for efficient downstream training
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## 📊 Dataset Statistics
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| Metric | Count |
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|-------------------------|-------------|
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| Total Entities | 2,306,921 |
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| Total Relations | 27,232,091 |
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| Connected Entities | 834,809 |
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| Connected Relations | 27,087,971 |
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| Entity Types | 11 |
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| Relation Types | 30 |
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---
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### 🧬 Entity Types
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| Entity Type | Count | Percentage (%) | Connected Count | Connected (%) |
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|--------------|-----------|----------------|------------------|----------------|
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| Promoter | 230,358 | 9.99 | 86,238 | 10.33 |
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| Gene | 230,358 | 9.99 | 86,238 | 10.33 |
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| Transcript | 412,326 | 17.87 | 412,039 | 49.36 |
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| Protein | 173,978 | 7.54 | 121,419 | 14.54 |
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| Pathway | 6,793 | 0.29 | 1,930 | 0.23 |
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| Metabolite | 218,335 | 9.46 | 62,364 | 7.47 |
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| Microbiota | 621,882 | 26.96 | 1,119 | 0.13 |
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| Exposure | 1,159 | 0.05 | 1,037 | 0.12 |
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| Phenotype | 19,532 | 0.85 | 19,078 | 2.29 |
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| Disease | 118,814 | 5.15 | 22,429 | 2.69 |
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| Drug | 273,386 | 11.85 | 20,918 | 2.51 |
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| **Total** | **2,306,921** | **100** | **834,809** | **100** |
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---
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### 🔗 Relation Types
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| Relation Type | Count | Percentage (%) |
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|------------------------|-------------|----------------|
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| Promoter-Gene | 230,358 | 0.85 |
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| Gene-Transcript | 427,810 | 1.57 |
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| Transcript-Protein | 152,585 | 0.56 |
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| Protein-Protein | 16,484,820 | 60.53 |
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| Protein-Pathway | 152,912 | 0.56 |
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| Protein-Phenotype | 478,279 | 1.76 |
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| Protein-Disease | 143,394 | 0.53 |
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| Pathway-Protein | 176,133 | 0.65 |
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| Pathway-Drug | 1,795 | 0.01 |
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| Pathway-Exposure | 301,448 | 1.11 |
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| Metabolite-Protein | 2,804,430 | 10.30 |
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| Metabolite-Pathway | 12,198 | 0.04 |
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| Metabolite-Metabolite | 931 | 0.003 |
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| Metabolite-Disease | 24,970 | 0.09 |
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| Microbiota-Disease | 22,371 | 0.08 |
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| Microbiota-Drug | 866 | 0.003 |
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| Exposure-Gene | 28,982 | 0.11 |
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| Exposure-Pathway | 301,448 | 1.11 |
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| Exposure-Disease | 979,780 | 3.60 |
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| Phenotype-Phenotype | 23,427 | 0.09 |
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| Phenotype-Disease | 181,192 | 0.67 |
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| Disease-Phenotype | 181,192 | 0.67 |
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| Disease-Disease | 12,006 | 0.04 |
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| Drug-Protein | 84,859 | 0.31 |
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| Drug-Pathway | 3,065 | 0.01 |
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| Drug-Metabolite | 3,589 | 0.01 |
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| Drug-Microbiota | 866 | 0.003 |
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| Drug-Phenotype | 93,826 | 0.34 |
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| Drug-Disease | 39,977 | 0.15 |
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| Drug-Drug | 3,882,582 | 14.26 |
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| **Total** | **27,232,091** | **100** |
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---
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## 📦 Access and Downloads
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- **Knowledge Graph Dataset**: [Hugging Face](https://huggingface.co/datasets/FuhaiLiAiLab/BioMedGraphica)
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- **Software & Tutorials**: [GitHub](https://github.com/FuhaiLiAiLab/BioMedGraphica)
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## 🧪 Validation
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- Hard matching for structured identifiers (e.g. Ensembl, HGNC)
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- BioBERT-based soft matching for flexible terms (e.g., diseases, phenotypes, drugs)
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- Case study and benchmarking with Synapse dataset
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## 📚 Citation
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```
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@article{zhang2024biomedgraphica,
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title={BioMedGraphica: An All-in-One Platform for Biomedical Prior Knowledge and Omic Signaling Graph Generation},
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author={Zhang, Heming and Liang, Shunning and Xu, Tim and Li, Wenyu and Huang, Di and Dong, Yuhan and Li, Guangfu and Miller, J Philip and Goedegebuure, S Peter and Sardiello, Marco and others},
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journal={bioRxiv},
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year={2024}
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}
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```
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