https://github.com/lakkevivek/genomic-interpreter

Automates the analysis of complex genomic data using the 1D-Swin Transformer model.Designed for researchers to interpret large DNA sequences more accurately and efficiently.Visualizes mutation patterns and genetic markers using interactive dashboards in Streamlit.Supports integration with external genomic databases like NCBI for cross-referencing
https://github.com/lakkevivek/genomic-interpreter

1d-swin-transformer python streamlit

Last synced: about 1 month ago
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Automates the analysis of complex genomic data using the 1D-Swin Transformer model.Designed for researchers to interpret large DNA sequences more accurately and efficiently.Visualizes mutation patterns and genetic markers using interactive dashboards in Streamlit.Supports integration with external genomic databases like NCBI for cross-referencing

• Host: GitHub
• URL: https://github.com/lakkevivek/genomic-interpreter
• Owner: lakkevivek
• Created: 2025-04-22T15:00:47.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-04-22T15:15:53.000Z (about 1 year ago)
• Last Synced: 2025-04-23T15:16:32.160Z (about 1 year ago)
• Topics: 1d-swin-transformer, python, streamlit
• Language: Python
• Homepage:
• Size: 1.41 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Genomic-Interpreter-Transformer

Attention-based hierarchical Transformer (1D-Swin) for DNA sequence analysis. 

This project implements a **1D Swin Transformer**, a variation of the original [Swin Transformer](https://arxiv.org/abs/2103.14030) architecture adapted for 1D input data such as time-series, sensor signals, or sequence modeling tasks.



  

  

  

  

  

  



## Tech Stack 💻

* **Python:** 🐍 The primary programming language.

* **PyTorch:** 🔥 A deep learning framework used for building and training the 1D-Swin Transformer model.

* **Streamlit:** 🎈 A Python library for creating the web application interface.

* **PIL (Pillow):** 🖼️ Python Imaging Library, used for handling and displaying images.

* **matplotlib:** 📊 A plotting library used for visualizing DNA sequences and model outputs.

* **NumPy:** 🔢 A library for numerical computations, especially for handling arrays.

## Project Structure 📂

Genomic-Interpreter-Transformer/  

│  

├── app.py              # 📱 Streamlit app - user interface  

├── demo.py             # 🧠 Model implementation and logic  

├── README.md           # 📝 Project description  

├── swin1d/             # ⚙️ Directory for the Swin1D model  

│   ├── module.py       # 🏗️ Swin1D model definition  

│   └── examples.py     # 🔧 Helper functions (e.g., one-hot encoding)  

│  

└── requirements.txt    # 📦 Project dependencies  

## Installation Guide 🛠️

1.  **Clone the repository:**

    ```bash

    git clone 

    cd Genomic-Interpreter-Transformer

    ```

2.  **Create a virtual environment (recommended):**

    ```bash

    python3 -m venv venv

    source venv/bin/activate  # 🐧 On Linux/macOS

    venv\Scripts\activate  # 🪟 On Windows

    ```

3.  **Install dependencies:**

    ```bash

    pip install -r requirements.txt

    ```

4.  **Run the Streamlit app:**

    ```bash

    streamlit run app.py

    ```

    This will open the application in your web browser. 🌐

## Dependencies 📦

* Python 3.x

* PyTorch

* Streamlit

* Pillow (PIL)

* matplotlib

* NumPy

## Future Directions 🚀

* **Model Improvement:**

    * Explore different Transformer architectures or hyperparameters for better accuracy. 🎯

    * Train the model on a larger and more diverse dataset of genomic sequences. 🧬

    * Incorporate other biological data (e.g., gene annotations) into the model. 🔬

* **Feature Expansion:**

    * Add functionality to predict specific genomic features (e.g., gene locations, mutations). 🔍

    * Develop more sophisticated visualizations of the model's output. 📈

    * Support different input formats (e.g., FASTA files). 📂

* **User Interface:**

    * Enhance the Streamlit app with more interactive elements. 🖱️

    * Allow users to upload their own genomic data. ⬆️

    * Deploy the app online for broader accessibility. ☁️

* **Performance Optimization:**

    * Optimize the code for faster processing, especially for longer sequences. ⚡

    * Consider using GPUs for accelerated model inference. 🏎️

## Conclusion ✅

This project demonstrates the potential of Transformer models for analyzing DNA sequences. The Genomic Interpreter provides a user-friendly interface for visualizing genomic data, offering insights into sequence patterns. Future work will focus on improving the model's accuracy, expanding its functionality, and enhancing the user experience. This tool has the potential to aid researchers in genomic analysis and contribute to a deeper understanding of biological information. 🌟