Upload README.md with huggingface_hub

README.md

@@ -1,3 +1,133 @@
 ---
+annotations_creators:
+- expert-generated
+language:
+- en
 license: mit
+pretty_name: BioMedGraphica
+tags:
+- biomedical
+- knowledge-graph
+- multi-omics
+- data-integration
+- graph-machine-learning
+- drug-discovery
+- named-entity-recognition
+- large-language-models
+size_categories:
+- 1M<n<10M
+task_categories:
+- graph-machine-learning
+- text-classification
+- named-entity-recognition
 ---
+
+# 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 TCGA and ROSMAP datasets
+
+## 📚 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}
+}
+```
+
+