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----
-license: mit
-tags:
-- cancer-genomics
-- bioinformatics
-- graph-database
-- neo4j
-- distributed-computing
-- boinc
-- healthcare
-- genomics
-- fastq
-- blast
-- variant-calling
-- gdc-portal
-- tcga
-library_name: fastapi
-pipeline_tag: other
----
-# Cancer@Home v2
-<div align="center">
-  <img src="https://img.shields.io/badge/version-2.0.0-blue.svg" alt="Version">
-  <img src="https://img.shields.io/badge/license-MIT-green.svg" alt="License">
-  <img src="https://img.shields.io/badge/python-3.8+-blue.svg" alt="Python">
-  <img src="https://img.shields.io/badge/neo4j-5.13-brightgreen.svg" alt="Neo4j">
-</div>
-## 🧬 Overview
-Cancer@Home v2 is a comprehensive distributed computing platform for cancer genomics research that combines **BOINC distributed computing**, **GDC cancer data analysis**, **sequence processing (FASTQ/BLAST)**, and **Neo4j graph visualization** into a unified, easy-to-use system.
-Inspired by [Cancer@Home v1](https://www.herox.com/DCx/round/516/entry/23285) and [Andrew Kamal's Neo4j Dashboard](https://medium.com/neo4j/visualize-cancer-1c80a95f5bb4), this platform makes cancer genomics research accessible, distributed, and visual.
-## 🎯 Key Features
-- 🌐 **Interactive Web Dashboard** - Modern UI with real-time visualizations
-- 🔍 **Neo4j Graph Database** - Model complex gene-mutation-patient relationships
-- ⚡ **BOINC Integration** - Distributed computing for intensive analyses
-- 📊 **GraphQL API** - Flexible data querying
-- 🧪 **Bioinformatics Pipeline** - FASTQ processing, BLAST alignment, variant calling
-- 📚 **GDC Portal Integration** - Access TCGA/TARGET cancer datasets
-- 🚀 **Quick Setup** - Running in under 5 minutes
-## 🏗️ Architecture
-```
-┌─────────────────────────────────────────────┐
-│     Web Dashboard (D3.js + Chart.js)        │
-├─────────────────────────────────────────────┤
-│     FastAPI Backend (REST + GraphQL)        │
-├──────┬──────┬──────┬──────┬────────────────┤
-│Neo4j │BOINC │ GDC  │FASTQ │ BLAST/Variant  │
-│Graph │Client│ API  │  QC  │    Calling     │
-└──────┴──────┴──────┴──────┴────────────────┘
-```
-## 📦 Installation
-### Prerequisites
-- Python 3.8+
-- Docker Desktop
-- 8GB RAM (16GB recommended)
-### Quick Start
-**Windows:**
-```powershell
-git clone https://huggingface.co/OpenPeerAI/CancerAtHomeV2
-cd CancerAtHomeV2
-.\setup.ps1
-python run.py
-```
-**Linux/Mac:**
-```bash
-git clone https://huggingface.co/OpenPeerAI/CancerAtHomeV2
-cd CancerAtHomeV2
-chmod +x setup.sh
-./setup.sh
-python run.py
-```
-Then open: **http://localhost:5000**
-## 🚀 Usage
-### Web Dashboard
-Access the interactive dashboard at http://localhost:5000 with:
-- **Dashboard Tab**: Overview statistics and mutation charts
-- **Neo4j Visualization**: Interactive graph of cancer relationships
-- **BOINC Tasks**: Submit and monitor distributed computing tasks
-- **GDC Data**: Browse and download cancer datasets
-- **Pipeline Tools**: Run FASTQ QC, BLAST, and variant calling
-### GraphQL API
-Query cancer data at http://localhost:5000/graphql
-**Example: Get mutations in TP53 gene**
-```graphql
-query {
-  mutations(gene: "TP53") {
-    mutation_id
-    chromosome
-    position
-    consequence
-  }
-}
-```
-**Example: Get patient statistics**
-```graphql
-query {
-  cancerStatistics(cancer_type_id: "BRCA") {
-    total_patients
-    total_mutations
-    avg_mutations_per_patient
-  }
-}
-```
-### REST API
-**Database Summary:**
-```bash
-curl http://localhost:5000/api/neo4j/summary
-```
-**Submit BOINC Task:**
-```bash
-curl -X POST http://localhost:5000/api/boinc/submit \
-  -H "Content-Type: application/json" \
-  -d '{"workunit_type": "variant_calling", "input_file": "sample.fastq"}'
-```
-### Python API
-**FASTQ Processing:**
-```python
-from backend.pipeline import FASTQProcessor
-processor = FASTQProcessor()
-stats = processor.calculate_statistics("input.fastq")
-filtered = processor.quality_filter("input.fastq")
-```
-**Variant Calling:**
-```python
-from backend.pipeline import VariantCaller, VariantAnalyzer
-caller = VariantCaller()
-vcf_file = caller.call_variants("alignment.bam", "reference.fa")
-variants = caller.filter_variants(vcf_file)
-analyzer = VariantAnalyzer()
-cancer_variants = analyzer.identify_cancer_variants(variants)
-tmb = analyzer.calculate_mutation_burden(variants)
-```
-**Neo4j Queries:**
-```python
-from backend.neo4j import DatabaseManager
-db = DatabaseManager()
-query = """
-MATCH (g:Gene {symbol: 'TP53'})<-[:AFFECTS]-(m:Mutation)
-RETURN m.position, m.consequence
-"""
-results = db.execute_query(query)
-db.close()
-```
-## 📊 Data Model
-### Neo4j Graph Schema
-**Nodes:**
-- **Gene**: Genes with mutations (TP53, BRCA1, KRAS, etc.)
-- **Mutation**: Genetic variants with position and consequence
-- **Patient**: Individual cases with demographics
-- **CancerType**: Cancer classifications (BRCA, LUAD, COAD, GBM)
-**Relationships:**
-- `Gene ← AFFECTS ← Mutation`
-- `Patient → HAS_MUTATION → Mutation`
-- `Patient → DIAGNOSED_WITH → CancerType`
-### Sample Data Included
-- **7 Genes**: TP53, BRAF, BRCA1, BRCA2, PIK3CA, KRAS, EGFR
-- **5 Mutations**: Cancer-associated variants
-- **5 Patients**: Representative TCGA cases
-- **4 Cancer Types**: BRCA, LUAD, COAD, GBM
-## 🔧 Technology Stack
-- **Backend**: FastAPI, Python 3.8+
-- **Database**: Neo4j 5.13 (Graph Database)
-- **API**: GraphQL (Strawberry), REST
-- **Frontend**: HTML5, CSS3, JavaScript, D3.js, Chart.js
-- **Bioinformatics**: Biopython, BLAST+
-- **Data Source**: GDC Portal API (TCGA/TARGET)
-- **Infrastructure**: Docker, Docker Compose
-- **Distributed Computing**: BOINC Framework
-## 📚 Documentation
-- [README.md](README.md) - Complete project overview
-- [QUICKSTART.md](QUICKSTART.md) - 5-minute setup guide
-- [USER_GUIDE.md](USER_GUIDE.md) - Detailed usage documentation
-- [GRAPHQL_EXAMPLES.md](GRAPHQL_EXAMPLES.md) - Query examples
-- [ARCHITECTURE.md](ARCHITECTURE.md) - System architecture
-- [PROJECT_SUMMARY.md](PROJECT_SUMMARY.md) - Feature overview
-## 🎓 Use Cases
-1. **Cancer Research**: Analyze genomics data with distributed computing
-2. **Education**: Learn cancer genetics and bioinformatics
-3. **Data Visualization**: Explore gene-mutation-patient relationships
-4. **Pipeline Development**: Test bioinformatics workflows
-5. **Graph Analytics**: Query complex biological networks
-## 🔬 Supported Cancer Projects
-- **TCGA-BRCA**: Breast Cancer (1,098 cases)
-- **TCGA-LUAD**: Lung Adenocarcinoma (585 cases)
-- **TCGA-COAD**: Colon Adenocarcinoma (461 cases)
-- **TCGA-GBM**: Glioblastoma (617 cases)
-- **TARGET-AML**: Acute Myeloid Leukemia (238 cases)
-## 📈 Bioinformatics Pipeline
-### FASTQ Processing
-- Quality control and filtering
-- Adapter trimming
-- Statistics calculation
-- QC report generation
-### BLAST Alignment
-- BLASTN for nucleotide sequences
-- BLASTP for protein sequences
-- Hit filtering by identity/e-value
-- Homology detection
-### Variant Calling
-- VCF generation from alignments
-- Quality filtering
-- Cancer variant identification
-- Tumor mutation burden (TMB) calculation
-## 🌐 Access Points
-- **Application**: http://localhost:5000
-- **API Docs**: http://localhost:5000/docs (Swagger UI)
-- **GraphQL**: http://localhost:5000/graphql
-- **Neo4j Browser**: http://localhost:7474 (neo4j/cancer123)
-## 🛠️ Configuration
-Edit `config.yml` to customize:
-```yaml
-neo4j:
-  uri: "bolt://localhost:7687"
-  password: "cancer123"
-gdc:
-  download_dir: "./data/gdc"
-  projects: ["TCGA-BRCA", "TCGA-LUAD", "TCGA-COAD"]
-pipeline:
-  fastq:
-    quality_threshold: 20
-    min_length: 50
-  blast:
-    evalue: 0.001
-    num_threads: 4
-```
-## 🤝 Contributing
-Contributions are welcome! This project is open source under the MIT License.
-### Development Setup
-```bash
-python -m venv venv
-source venv/bin/activate  # or venv\Scripts\activate on Windows
-pip install -r requirements.txt
-pytest test_cancer_at_home.py
-```
-## 📄 License
-MIT License - See [LICENSE](LICENSE) file
-Copyright (c) 2025 OpenPeer AI, Riemann Computing Inc., Bleunomics, Andrew Magdy Kamal
-## 🙏 Acknowledgments
-### Inspiration
-- [Cancer@Home v1](https://www.herox.com/DCx/round/516/entry/23285) - HeroX DCx Challenge
-- [Andrew Kamal's Neo4j Cancer Visualization](https://medium.com/neo4j/visualize-cancer-1c80a95f5bb4)
-### Data Sources
-- [Genomic Data Commons (GDC) Portal](https://portal.gdc.cancer.gov/)
-- The Cancer Genome Atlas (TCGA) Program
-- Therapeutically Applicable Research to Generate Effective Treatments (TARGET)
-### Technologies
-- Neo4j Graph Database
-- BOINC Distributed Computing Project
-- Biopython Community
-- FastAPI Framework
-## 👥 Authors
-- **OpenPeer AI** - Core development and architecture
-- **Riemann Computing Inc.** - Distributed computing integration
-- **Bleunomics** - Bioinformatics pipeline and genomics expertise
-- **Andrew Magdy Kamal** - Graph database design and visualization
-## 📞 Support
-- **Documentation**: See project documentation files
-- **Issues**: Check logs in `logs/cancer_at_home.log`
-- **Configuration**: Review `config.yml`
-- **Health Check**: http://localhost:5000/api/health
-## 🔮 Roadmap
-### Planned Features
-- Machine learning for mutation prediction
-- Multi-omics data integration (RNA-seq, proteomics)
-- Survival analysis and clinical outcomes
-- Advanced graph algorithms (PageRank, community detection)
-- Cloud deployment support (AWS, Azure, GCP)
-- Mobile-responsive design
-- User authentication and authorization
-## 📊 Statistics
-- **Lines of Code**: ~5,000+
-- **Modules**: 9 Python modules
-- **API Endpoints**: 15+ REST + GraphQL
-- **Documentation**: 2,500+ lines
-- **Setup Time**: < 5 minutes
-- **Sample Data**: 7 genes, 5 mutations, 5 patients
-## 🎯 Citation
-If you use Cancer@Home v2 in your research, please cite:
-```bibtex
-@software{cancer_at_home_v2,
-  title = {Cancer@Home v2: Distributed Cancer Genomics Research Platform},
-  author = {OpenPeer AI and Riemann Computing Inc. and Bleunomics and Andrew Magdy Kamal},
-  year = {2025},
-  url = {https://huggingface.co/OpenPeerAI/CancerAtHomeV2},
-  license = {MIT}
-}
-```
-## 🏷️ Tags
-`cancer-genomics` `bioinformatics` `neo4j` `graph-database` `distributed-computing` `boinc` `fastq` `blast` `variant-calling` `gdc-portal` `tcga` `target` `graphql` `fastapi` `python` `docker` `healthcare` `precision-medicine` `computational-biology`
----
-**Made with ❤️ by OpenPeer AI, Riemann Computing Inc., Bleunomics, and Andrew Magdy Kamal**
-**For cancer research, by researchers, accessible to all.**

+---
+license: mit
+tags:
+- cancer-genomics
+- bioinformatics
+- graph-database
+- neo4j
+- distributed-computing
+- boinc
+- healthcare
+- genomics
+- fastq
+- blast
+- variant-calling
+- gdc-portal
+- tcga
+library_name: cancer-at-home-v2
+pipeline_tag: other
+---
+# Cancer@Home v2
+<div align="center">
+  <img src="https://img.shields.io/badge/version-2.0.0-blue.svg" alt="Version">
+  <img src="https://img.shields.io/badge/license-MIT-green.svg" alt="License">
+  <img src="https://img.shields.io/badge/python-3.8+-blue.svg" alt="Python">
+  <img src="https://img.shields.io/badge/neo4j-5.13-brightgreen.svg" alt="Neo4j">
+</div>
+## 🧬 Overview
+Cancer@Home v2 is a comprehensive distributed computing platform for cancer genomics research that combines **BOINC distributed computing**, **GDC cancer data analysis**, **sequence processing (FASTQ/BLAST)**, and **Neo4j graph visualization** into a unified, easy-to-use system.
+Inspired by [Cancer@Home v1](https://www.herox.com/DCx/round/516/entry/23285) and [Andrew Kamal's Neo4j Dashboard](https://medium.com/neo4j/visualize-cancer-1c80a95f5bb4), this platform makes cancer genomics research accessible, distributed, and visual.
+## 🎯 Key Features
+- 🌐 **Interactive Web Dashboard** - Modern UI with real-time visualizations
+- 🔍 **Neo4j Graph Database** - Model complex gene-mutation-patient relationships
+- ⚡ **BOINC Integration** - Distributed computing for intensive analyses
+- 📊 **GraphQL API** - Flexible data querying
+- 🧪 **Bioinformatics Pipeline** - FASTQ processing, BLAST alignment, variant calling
+- 📚 **GDC Portal Integration** - Access TCGA/TARGET cancer datasets
+- 🚀 **Quick Setup** - Running in under 5 minutes
+## 🏗️ Architecture
+```
+┌─────────────────────────────────────────────┐
+│     Web Dashboard (D3.js + Chart.js)        │
+├─────────────────────────────────────────────┤
+│     FastAPI Backend (REST + GraphQL)        │
+├──────┬──────┬──────┬──────┬────────────────┤
+│Neo4j │BOINC │ GDC  │FASTQ │ BLAST/Variant  │
+│Graph │Client│ API  │  QC  │    Calling     │
+└──────┴──────┴──────┴──────┴────────────────┘
+```
+## 📦 Installation
+### Prerequisites
+- Python 3.8+
+- Docker Desktop
+- 8GB RAM (16GB recommended)
+### Quick Start
+**Windows:**
+```powershell
+git clone https://huggingface.co/OpenPeerAI/CancerAtHomeV2
+cd CancerAtHomeV2
+.\setup.ps1
+python run.py
+```
+**Linux/Mac:**
+```bash
+git clone https://huggingface.co/OpenPeerAI/CancerAtHomeV2
+cd CancerAtHomeV2
+chmod +x setup.sh
+./setup.sh
+python run.py
+```
+Then open: **http://localhost:5000**
+## 🚀 Usage
+### Web Dashboard
+Access the interactive dashboard at http://localhost:5000 with:
+- **Dashboard Tab**: Overview statistics and mutation charts
+- **Neo4j Visualization**: Interactive graph of cancer relationships
+- **BOINC Tasks**: Submit and monitor distributed computing tasks
+- **GDC Data**: Browse and download cancer datasets
+- **Pipeline Tools**: Run FASTQ QC, BLAST, and variant calling
+### GraphQL API
+Query cancer data at http://localhost:5000/graphql
+**Example: Get mutations in TP53 gene**
+```graphql
+query {
+  mutations(gene: "TP53") {
+    mutation_id
+    chromosome
+    position
+    consequence
+  }
+}
+```
+**Example: Get patient statistics**
+```graphql
+query {
+  cancerStatistics(cancer_type_id: "BRCA") {
+    total_patients
+    total_mutations
+    avg_mutations_per_patient
+  }
+}
+```
+### REST API
+**Database Summary:**
+```bash
+curl http://localhost:5000/api/neo4j/summary
+```
+**Submit BOINC Task:**
+```bash
+curl -X POST http://localhost:5000/api/boinc/submit \
+  -H "Content-Type: application/json" \
+  -d '{"workunit_type": "variant_calling", "input_file": "sample.fastq"}'
+```
+### Python API
+**FASTQ Processing:**
+```python
+from backend.pipeline import FASTQProcessor
+processor = FASTQProcessor()
+stats = processor.calculate_statistics("input.fastq")
+filtered = processor.quality_filter("input.fastq")
+```
+**Variant Calling:**
+```python
+from backend.pipeline import VariantCaller, VariantAnalyzer
+caller = VariantCaller()
+vcf_file = caller.call_variants("alignment.bam", "reference.fa")
+variants = caller.filter_variants(vcf_file)
+analyzer = VariantAnalyzer()
+cancer_variants = analyzer.identify_cancer_variants(variants)
+tmb = analyzer.calculate_mutation_burden(variants)
+```
+**Neo4j Queries:**
+```python
+from backend.neo4j import DatabaseManager
+db = DatabaseManager()
+query = """
+MATCH (g:Gene {symbol: 'TP53'})<-[:AFFECTS]-(m:Mutation)
+RETURN m.position, m.consequence
+"""
+results = db.execute_query(query)
+db.close()
+```
+## 📊 Data Model
+### Neo4j Graph Schema
+**Nodes:**
+- **Gene**: Genes with mutations (TP53, BRCA1, KRAS, etc.)
+- **Mutation**: Genetic variants with position and consequence
+- **Patient**: Individual cases with demographics
+- **CancerType**: Cancer classifications (BRCA, LUAD, COAD, GBM)
+**Relationships:**
+- `Gene ← AFFECTS ← Mutation`
+- `Patient → HAS_MUTATION → Mutation`
+- `Patient → DIAGNOSED_WITH → CancerType`
+### Sample Data Included
+- **7 Genes**: TP53, BRAF, BRCA1, BRCA2, PIK3CA, KRAS, EGFR
+- **5 Mutations**: Cancer-associated variants
+- **5 Patients**: Representative TCGA cases
+- **4 Cancer Types**: BRCA, LUAD, COAD, GBM
+## 🔧 Technology Stack
+- **Backend**: FastAPI, Python 3.8+
+- **Database**: Neo4j 5.13 (Graph Database)
+- **API**: GraphQL (Strawberry), REST
+- **Frontend**: HTML5, CSS3, JavaScript, D3.js, Chart.js
+- **Bioinformatics**: Biopython, BLAST+
+- **Data Source**: GDC Portal API (TCGA/TARGET)
+- **Infrastructure**: Docker, Docker Compose
+- **Distributed Computing**: BOINC Framework
+## 📚 Documentation
+- [README.md](README.md) - Complete project overview
+- [QUICKSTART.md](QUICKSTART.md) - 5-minute setup guide
+- [USER_GUIDE.md](USER_GUIDE.md) - Detailed usage documentation
+- [GRAPHQL_EXAMPLES.md](GRAPHQL_EXAMPLES.md) - Query examples
+- [ARCHITECTURE.md](ARCHITECTURE.md) - System architecture
+- [PROJECT_SUMMARY.md](PROJECT_SUMMARY.md) - Feature overview
+## 🎓 Use Cases
+1. **Cancer Research**: Analyze genomics data with distributed computing
+2. **Education**: Learn cancer genetics and bioinformatics
+3. **Data Visualization**: Explore gene-mutation-patient relationships
+4. **Pipeline Development**: Test bioinformatics workflows
+5. **Graph Analytics**: Query complex biological networks
+## 🔬 Supported Cancer Projects
+- **TCGA-BRCA**: Breast Cancer (1,098 cases)
+- **TCGA-LUAD**: Lung Adenocarcinoma (585 cases)
+- **TCGA-COAD**: Colon Adenocarcinoma (461 cases)
+- **TCGA-GBM**: Glioblastoma (617 cases)
+- **TARGET-AML**: Acute Myeloid Leukemia (238 cases)
+## 📈 Bioinformatics Pipeline
+### FASTQ Processing
+- Quality control and filtering
+- Adapter trimming
+- Statistics calculation
+- QC report generation
+### BLAST Alignment
+- BLASTN for nucleotide sequences
+- BLASTP for protein sequences
+- Hit filtering by identity/e-value
+- Homology detection
+### Variant Calling
+- VCF generation from alignments
+- Quality filtering
+- Cancer variant identification
+- Tumor mutation burden (TMB) calculation
+## 🌐 Access Points
+- **Application**: http://localhost:5000
+- **API Docs**: http://localhost:5000/docs (Swagger UI)
+- **GraphQL**: http://localhost:5000/graphql
+- **Neo4j Browser**: http://localhost:7474 (neo4j/cancer123)
+## 🛠️ Configuration
+Edit `config.yml` to customize:
+```yaml
+neo4j:
+  uri: "bolt://localhost:7687"
+  password: "cancer123"
+gdc:
+  download_dir: "./data/gdc"
+  projects: ["TCGA-BRCA", "TCGA-LUAD", "TCGA-COAD"]
+pipeline:
+  fastq:
+    quality_threshold: 20
+    min_length: 50
+  blast:
+    evalue: 0.001
+    num_threads: 4
+```
+## 🤝 Contributing
+Contributions are welcome! This project is open source under the MIT License.
+### Development Setup
+```bash
+python -m venv venv
+source venv/bin/activate  # or venv\Scripts\activate on Windows
+pip install -r requirements.txt
+pytest test_cancer_at_home.py
+```
+## 📄 License
+MIT License - See [LICENSE](LICENSE) file
+Copyright (c) 2025 OpenPeer AI, Riemann Computing Inc., Bleunomics, Andrew Magdy Kamal
+## 🙏 Acknowledgments
+### Inspiration
+- [Cancer@Home v1](https://www.herox.com/DCx/round/516/entry/23285) - HeroX DCx Challenge
+- [Andrew Kamal's Neo4j Cancer Visualization](https://medium.com/neo4j/visualize-cancer-1c80a95f5bb4)
+### Data Sources
+- [Genomic Data Commons (GDC) Portal](https://portal.gdc.cancer.gov/)
+- The Cancer Genome Atlas (TCGA) Program
+- Therapeutically Applicable Research to Generate Effective Treatments (TARGET)
+### Technologies
+- Neo4j Graph Database
+- BOINC Distributed Computing Project
+- Biopython Community
+- FastAPI Framework
+## 👥 Authors
+- **OpenPeer AI** - Core development and architecture
+- **Riemann Computing Inc.** - Distributed computing integration
+- **Bleunomics** - Bioinformatics pipeline and genomics expertise
+- **Andrew Magdy Kamal** - Graph database design and visualization
+## 📞 Support
+- **Documentation**: See project documentation files
+- **Issues**: Check logs in `logs/cancer_at_home.log`
+- **Configuration**: Review `config.yml`
+- **Health Check**: http://localhost:5000/api/health
+## 🔮 Roadmap
+### Planned Features
+- Machine learning for mutation prediction
+- Multi-omics data integration (RNA-seq, proteomics)
+- Survival analysis and clinical outcomes
+- Advanced graph algorithms (PageRank, community detection)
+- Cloud deployment support (AWS, Azure, GCP)
+- Mobile-responsive design
+- User authentication and authorization
+## 📊 Statistics
+- **Lines of Code**: ~5,000+
+- **Modules**: 9 Python modules
+- **API Endpoints**: 15+ REST + GraphQL
+- **Documentation**: 2,500+ lines
+- **Setup Time**: < 5 minutes
+- **Sample Data**: 7 genes, 5 mutations, 5 patients
+## 🎯 Citation
+If you use Cancer@Home v2 in your research, please cite:
+```bibtex
+@software{cancer_at_home_v2,
+  title = {Cancer@Home v2: Distributed Cancer Genomics Research Platform},
+  author = {OpenPeer AI and Riemann Computing Inc. and Bleunomics and Andrew Magdy Kamal},
+  year = {2025},
+  url = {https://huggingface.co/OpenPeerAI/CancerAtHomeV2},
+  license = {MIT}
+}
+```
+## 🏷️ Tags
+`cancer-genomics` `bioinformatics` `neo4j` `graph-database` `distributed-computing` `boinc` `fastq` `blast` `variant-calling` `gdc-portal` `tcga` `target` `graphql` `fastapi` `python` `docker` `healthcare` `precision-medicine` `computational-biology`
+---
+**Made with ❤️ by OpenPeer AI, Riemann Computing Inc., Bleunomics, and Andrew Magdy Kamal**
+**For cancer research, by researchers, accessible to all.**