DNA, RNA, and Protein Classification Project

This project focuses on classifying DNA, RNA, and protein sequences using a combination of machine learning and deep learning techniques. It includes a backend server, a Vue.js-based frontend, and a Jupyter Notebook for model training and testing. For a detailed walkthrough, watch this introduction video on YouTube.

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Project Structure

project-directory/
├── frontend/       # Frontend application built with Vue.js
├── backend/        # Backend server implemented in Python
├── models.ipynb    # Jupyter Notebook for model training and evaluation

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Getting Started

Backend Setup

1. Navigate to the backend folder:

   cd backend

2. Install the required dependencies:

   pip install -r requirements.txt

3. Run the backend server using Python:

   python app.py

   The backend will start listening for requests at http://127.0.0.1:5001.

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Frontend Setup

1. Navigate to the frontend folder:

   cd frontend

2. Ensure you are using Node.js version 16 (using nvm):

   nvm use 16

3. Install dependencies:

   npm install

4. Start the development server:

   npm run dev

   The frontend will run locally, typically accessible at http://localhost:8080.

   The frontend is styled using the open-source template smpe-admin-web for an enhanced user experience.

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Using the Application

Once both the backend and frontend services are running:

1. Open the frontend application in your web browser.
2. Click Function -> Classification
3. Input input the structureId, chainId, sequence, residueCount of the macromolecule.
4. Choose the model you want.
5. Click the button Classify to get the classification result.

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Model Training and Testing

The models.ipynb file contains the full process of training, optimizing, and evaluating machine learning and deep learning models for sequence classification. It includes:

• Data preprocessing steps.
• Model selection and hyperparameter tuning.
• Performance evaluation with test results.

To view and run the notebook:

1. Open the file in Jupyter Notebook or any compatible IDE (e.g., JupyterLab, VSCode).
2. Execute the cells to reproduce the training and testing process.

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Dependencies

Backend:

• Python 3.x
• Flask
• scikit-learn
• PyTorch

Frontend:

• Node.js (version 16)
• Vue.js
• Dependencies listed in package.json

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Notes

• Ensure that the backend server is running before using the frontend to ensure proper functionality.
• Model training is computationally intensive; it is recommended to use a machine with GPU acceleration for faster training times.

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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Acknowledgments

Special thanks to the creators of the smpe-admin-web template for their excellent frontend design.