# Acknowledgments

This project builds upon the work of many contributors in the precision medicine and bioinformatics communities.

## Core Technologies

### Model Context Protocol (MCP)
- **Anthropic** - For creating the Model Context Protocol specification and fostering the MCP ecosystem
- **MCP Specification** - [modelcontextprotocol.io](https://modelcontextprotocol.io/specification/2025-06-18)
- **FastMCP Framework** - Python SDK for building MCP servers ([GitHub](https://github.com/modelcontextprotocol/python-sdk))

### Bioinformatics Research
- **BioinfoMCP Research Team** - Pioneering work on bioinformatics workflows with MCP ([arXiv:2510.02139](https://arxiv.org/html/2510.02139v1))
- **FGbio Toolkit** - FASTQ and VCF quality control utilities ([GitHub](https://github.com/fulcrumgenomics/fgbio))

## Data & Algorithms

### Public Databases
- **The Cancer Genome Atlas (TCGA)** - NIH/NCI public cancer genomics datasets, particularly TCGA-OV ovarian cancer cohort
- **GENCODE** - Human genome annotation consortium
- **KEGG (Kyoto Encyclopedia of Genes and Genomes)** - Pathway database for biological interpretation

### Spatial Transcriptomics
- **10x Genomics** - Visium spatial transcriptomics platform and public datasets
- **STAR Aligner** - RNA-seq alignment algorithm (Dobin et al., 2013)
- **ComBat** - Batch effect correction algorithm (Johnson et al., 2007)

### Multi-Omics Analysis
- **HAllA** - Hierarchical All-against-All association discovery (Rahnavard et al., 2017)
- **Stouffer's Method** - Meta-analysis for combining p-values across omics layers

## Community & Support

### Open Source Projects
This project integrates and builds upon numerous open-source bioinformatics tools:
- **Scanpy** - Single-cell analysis in Python
- **AnnData** - Annotated data structures for computational biology
- **pandas, NumPy, SciPy** - Scientific computing foundation
- **Matplotlib, Seaborn** - Visualization libraries

### Research Infrastructure
- **Google Cloud Healthcare API** - FHIR data management and de-identification
- **Google Cloud Vertex AI** - Scalable ML infrastructure
- **Hugging Face** - ML model hosting and inference APIs

## Personal Dedication

### PatientOne — Our Inspiration
This project is dedicated to the memory of a dear friend who passed away from High-Grade Serous Ovarian Carcinoma (HGSOC) in 2025. Her courage and grace throughout her battle with cancer inspired the creation of these tools.

**PatientOne** represents:
- The first patient in a precision medicine journey
- A reminder that behind every dataset is a human story
- Hope that AI-orchestrated bioinformatics can accelerate research and improve outcomes for future patients

*"In memory of strength, hope, and the pursuit of better treatments for all patients fighting ovarian cancer."*

PatientOne passed away in August 2025. Before her death, she asked that her story be used in any way that could mitigate suffering for others. This platform, and every patient it may one day help, exists in part because of her generosity and courage. We carry her forward in this work.

Before adding or modifying any content in docs/for-patients/, re-read this section.

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## Citation

If you use this repository in your research, please cite:

```bibtex
@software{langit2026precision,
  author = {Langit, Lynn},
  title = {Precision Medicine MCP Servers: AI-Orchestrated Clinical Bioinformatics},
  year = {2026},
  url = {https://github.com/lynnlangit/precision-medicine-mcp},
  note = {PatientOne - In memory of a dear friend}
}
```

## Contributing

We welcome contributions from the precision medicine and bioinformatics communities. See the main [README](README.md) for how to get started.

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**Last Updated:** April 21, 2026
