README.md

metadata

title: MLATE
emoji: 🥼
colorFrom: red
colorTo: indigo
sdk: streamlit
sdk_version: 1.44.1
app_file: app.py
pinned: false
license: cc-by-nc-4.0
short_description: ML Applications in Tissue Engineering

👬 MLATE V3: Multi-Tissue Scaffold Prediction Platform

MLATE V3 is a fully integrated, machine learning-powered platform for predicting, optimizing, and generating detailed fabrication procedures for 3D-(bio)printed scaffolds in tissue engineering. This app enables researchers to input a wide range of biomaterials, cell lines, and printing parameters, and receive optimized scaffold compositions along with step-by-step printing instructions generated via Google Gemini.

📄 Rafieyan et al. (preprint, 2025). MLATE V3: A fully integrated Multi-Tissue, machine learning platform for prediction, optimization and generating procedures for fabricating 3D-(bio)printing scaffolds for tissue engineering

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🚀 Features

• 🔬 Predict scaffold quality based on printability and cell response
• 🧪 Optimize biomaterial concentrations, cell densities, and printing parameters using Optuna
• 🧠 Powered by two fine-tuned CatBoostClassifier models
• 📋 Automatically generates fabrication protocols with Gemini API
• 🔐 Enforces safe defaults and intelligent UI input validation
• 🧱 Uses a real-world, curated dataset of 2847 samples across multiple tissues and cell lines

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📂 Dataset

This project includes a publicly available dataset (Dataset.xlsx) containing:

• 123 biomaterials
• 175 cell lines
• 7 printing parameters
• Scaffold performance labels

The dataset is available in the Files and Versions tab of this Space.

You may also optionally add this to Hugging Face Datasets for broader access.

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⚙️ How It Works

1. Input: User selects biomaterials, cell line, and printing parameters with min/max/step values
2. Optimization: Optuna runs 50 trials to maximize predicted scaffold quality (WSSQ)
3. Prediction:
   • Two CatBoost models are used to predict:
     • Printability (3-class)
     • Cell Response (5-class)
   • Probabilistic predictions are mapped to expected scores
4. Scaffold Quality: A weighted combination of printability and cell response
5. Procedure Generation: A Gemini API prompt generates custom step-by-step fabrication instructions

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💻 Running Locally

Clone the repo and install dependencies:

git clone https://huggingface.co/spaces/your-username/MLATE-V3
cd MLATE-V3

# Create virtual environment (optional)
python -m venv venv
source venv/bin/activate  # or venv\Scripts\activate on Windows

# Install dependencies
pip install -r requirements.txt

# Set your Gemini API key
export GEMINI_API_KEY=your_key_here  # or set in .env

# Run the app
streamlit run app.py

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📜 License

This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0).

You are free to:

• Share and adapt the code
• Use the dataset for academic research

But:

• Commercial use is prohibited
• Citation is required (see below)

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📚 Citation

If you use MLATE V3 or its dataset in your research, please cite:

Rafieyan et al. (preprint, 2025).
MLATE V3: A fully integrated Multi-Tissue, machine learning platform for prediction, optimization and generating procedures for fabricating 3D-(bio)printing scaffolds for tissue engineering
(Preprint link to be added after publication)

BibTeX:

@article{rafieyan2025mlate,
  author  = {Rafieyan, Saeed and others},
  title   = {MLATE V3: A fully integrated Multi-Tissue, machine learning platform for prediction, optimization and generating procedures for fabricating 3D-(bio)printing scaffolds for tissue engineering},
  journal = {Preprint},
  year    = {2025}
}

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⚠️ Disclaimer

This tool is intended for research and academic use only. While we strive for accuracy, the predictions and fabrication procedures are generated using machine learning and language models and may contain errors or inconsistencies. The authors are not responsible for any unintended consequences arising from use of this tool in experimental or clinical settings.

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Developed by Saeed Rafieyan