enformer official rough

Introduction

The Enformer model is a neural network architecture based on the Transformer, designed to improve the accuracy of predicting gene expression from DNA sequences. It was introduced in the paper "Effective gene expression prediction from sequence by integrating long-range interactions" by Avsec et al., and the official weights were released by DeepMind.

Architecture

Enformer utilizes a Transformer-based architecture to enhance the prediction accuracy of gene expression. It integrates long-range interactions, which are crucial for understanding complex biological processes. This approach leverages the strengths of Transformers in handling sequential data, making it suitable for genomic analyses.

Training

The model was initially trained and developed by DeepMind, and the official weights have been ported to PyTorch. For detailed insights into the training process, users are encouraged to refer to the original paper published in Nature.

Guide: Running Locally

1. Installation: Clone the repository from GitHub and install the necessary dependencies. Ensure you have PyTorch installed.

   git clone https://github.com/lucidrains/enformer-pytorch
   cd enformer-pytorch
   pip install -r requirements.txt
   

2. Usage: Follow the usage instructions available in the enformer-pytorch README for running the model with your data.

3. Hardware Recommendations: For optimal performance, consider using cloud-based GPUs such as those offered by AWS, Google Cloud, or Azure.

License

The Enformer model is released under the CC BY 4.0 license, allowing users to share and adapt the material provided proper attribution is given.