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