BacDoc 🧫

A Database-Driven Platform for Bacterial Identification and Culture Media Recommendation

BacDoc is a free, web-based microbiology support platform that assists with bacterial identification and cultivation media prediction, especially for resource-limited laboratories and educational settings. Unlike expensive commercial systems, BacDoc connects organism identification directly to optimized growth media recommendations, including automatic volume scaling and hybrid media generation for unknown organisms.

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🔬 Why BacDoc?

• Advanced diagnostic systems like MALDI-TOF and VITEK 2 are expensive and inaccessible in many labs.
• Over 99% of bacteria cannot be cultured using standard media.
• Existing databases list media recipes but do not automatically link identification to cultivation guidance.

BacDoc bridges this gap.

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✨ Key Features

• 🔍 Fuzzy organism name matching (handles spelling errors)
• 🧪 Automated growth media recommendation
• 📏 Automatic media scaling (100 mL → 2 L+)
• 🧬 Unknown organism handling using phenotypic similarity scoring
• 🧩 Hybrid media generation from closest matching organisms
• 🌐 Web-based interface built with Flask
• 💾 Database: Centraldatabase.csv (~800 species)
• 💸 Completely free & open-source

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🧠 How It Works (Conceptual)

1. User enters an organism name or
2. Provides phenotypic parameters for unknown organisms
   (Gram reaction, morphology, oxygen requirement, pH, temperature, origin)
3. A rule-based weighted distance algorithm identifies closest matches
4. Media compositions are retrieved, merged, and scaled automatically

⚠️ This is a rule-based research prototype, not an AI or clinical diagnostic tool.

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🛠️ Tech Stack

• Backend: Python, Flask
• Frontend: HTML, CSS, JavaScript
• Database: CSV-based curated microbiology dataset (~800 species)
• Algorithms: Fuzzy string matching + weighted phenotypic distance scoring

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🚀 Running Locally

git clone https://github.com/StressedUnderAMountain/BacDoc.git
cd BacDoc
pip install -r requirements.txt
PhytonAILLm.py 

Then open: http://0.0.0.0:5000

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📚 Academic Context

Developed as part of a B.Sc. Microbiology dissertation
Karmaveer Bhaurao Patil College, Navi Mumbai
University of Mumbai (2024–2025).

⚠️ Disclaimer

BacDoc is intended for educational and research use only.
It is not a clinical diagnostic system yet.
All recommendations require experimental validation.

📄 License

MIT License — see LICENSE file for details.

👤 Author

Preston Joshua Menezes
Microbiology | Computational Biology | Open Science