---
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
configs:
  - config_name: preview
    data_files:
      - BioMedGraphica-Conn/Entity/BioMedGraphica_Conn_Entity.csv
---

# BioMedGraphica

<div align="center">
  <img src="https://github.com/FuhaiLiAiLab/BioMedGraphica/blob/main/Figures/BMG-logo.png?raw=true" alt="BMG-logo" width="60%">
</div>

<div align="center">
  <a href="https://github.com/FuhaiLiAiLab/BioMedGraphica">
    <img src="https://img.shields.io/badge/GitHub-BioMedGraphica-181717?logo=github" alt="GitHub Repo">
  </a>
  <a href="https://github.com/FuhaiLiAiLab/BioMedGraphica">
    <img src="https://img.shields.io/badge/WebUI-BioMedGraphica-red" alt="WebUI">
  </a>
  <a href="https://github.com/FuhaiLiAiLab/BioMedGraphica">
    <img src="https://img.shields.io/badge/Knowledge%20Graph-BioMedGraphica-blue" alt="Knowledge Graph">
  </a>
</div>
<div align="center">
  <a href="https://huggingface.co/datasets/FuhaiLiAiLab/BioMedGraphica">
    <img src="https://img.shields.io/badge/Hugging%20Face-Dataset-FFD21E?logo=huggingface" alt="Dataset">
  </a>
  <a href="https://www.biorxiv.org/content/10.1101/2024.12.05.627020v2">
    <img src="https://img.shields.io/badge/bioRxiv-Paper-6f42c1" alt="Paper">
  </a>
</div>

**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

![BMG-stat](https://github.com/FuhaiLiAiLab/BioMedGraphica/blob/main/Figures/Figure0-1.png?raw=true)

| 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}
}
```